Tuflow - User contributions [en]

Hardware Benchmarking - Results

2021-07-27T15:01:28Z

TuflowJoe: /* High End GPU Results */

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable"

! style="background-color:#005581; font-weight:bold; color:white;"| Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Runtime Combined (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|AMD Ryzen 9 5950X 16-Core Processor||3.4||32||3400||39.7||343.0||382.7||JG2
|-
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||2.9||16||2900||63.3||385.0||448.3||NCV
|-
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||4.1||16||4104||62.7||386.6||449.3||GZH
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|-
|}
 

=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||4.6||23.5||28.1||KW2
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||NVIDIA GeForce RTX 3070||8||5888||5.0||28.8||33.8||NCV
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||5.5||29.7||35.2||JG2
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||NVIDIA GeForce RTX 2060 SUPER (core 1647MHz, mem 1750MHz)||8||2176||5.6||33.4||39.1||GZH
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|Intel(R) Core(TM) i3-8100 CPU @ 3.60GHz ||NVIDIA Tesla K40c||12||2880||12.3||83.1||95.3||NWE
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|-
|}
 

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||158||1192.2||1350.2||CH3
|-
|Intel(R) Core(TM) i9-10900F CPU @ 3.70GHz ||NVIDIA GeForce RTX 3090||24||10496||162.3||1192.4||1354.7||LJA
|-
|Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz ||NVIDIA GeForce RTX 3090||24||10496||172.9||1249.8||1422.7||SIP
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||178.9||1363.9||1542.8||KW2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||248.7||1928.6||2177.3||JG2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|-
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking_(2018-03-AA) | Back to TUFLOW Benchmarking]]
}}

Running linked Flood Modeller - TUFLOW Models

2021-07-07T12:48:01Z

TuflowJoe:

==Introduction==
There are several different ways to run a linked Flood Modeller-TUFLOW or ISIS-TUFLOW model. 
This wiki page is intended to guide the user through the main methods of installing the link correctly and provide information on troubleshooting. 
Other resources for installation help include:
*[http://wiki.tuflow.com/index.php?title=TUFLOW_Licensing Installing a TUFLOW dongle]
*[http://wiki.tuflow.com/index.php?title=Running_TUFLOW Running a TUFLOW Model]
*[http://help.floodmodeller.com/floodmodeller/ Installing Flood Modeller]
*[http://help.floodmodeller.com/isis/ISIS.htm Installing ISIS (for backwards compatibility)]

[https://www.floodmodeller.com/en-gb/products/desktop/flood-modeller/flood-modeller-free/ Flood Modeller Free] and TUFLOW Free permit the running of linked models. Both Flood Modeller and TUFLOW Free versions have limitations on model size and simulation run time. 
Licensing considerations for running linked models have not been considered in this section. It was been assumed that the User has purchased the Flood Modeller-TUFLOW link module or is making use of the free versions.

==Methods to Run Linked Flood Modeller-TUFLOW models==
The four main methods for installing Flood Modeller and TUFLOW to run linked models are detailed below.
It is assumed for all methods that Flood Modeller has already been installed and TUFLOW has been downloaded.

===Set the TUFLOW Engine File Location in the Flood Modeller Interface===
Using the Flood Modeller interface to set the location of the TUFLOW engine files for the TUFLOW build you want to use, is the simplest approach to linking Flood Modeller and TUFLOW and does not duplicate files. This method is recommended if it is expected that the same versions of Flood Modeller and TUFLOW will be used consistently when running linked models.
 

1) Open the Flood Modeller software and in the 'Home' tab select the 'General' option. 
[[file:FM Home Tab.png|500px]] 

2) Select the 'Project Settings' sub-menu and within the TUFLOW Engine File Location choose to browse to the version of TUFLOW that you would like to link Flood Modeller to. Choose 'Open' and then 'OK'. 
[[file:FM TUFLOW Linking.png|500px]] 

3) Save the changes that you have made to the setup. This will update the settings file (formed.ini).

4) Restart Flood Modeller to effect the revised setting.

4) The linked model can then be run by opening the .ief file within the Flood Modeller Interface and clicking Run. 

===Set an Environment Variable===
Setting TUFLOW as an environment variable allows for simple installation and minimises duplication of files. This method is recommended if it is expected that the same versions of Flood Modeller and TUFLOW will be used consistently when running linked models.
 
1) Click on the start button in windows and open the Control Panel. In the Control Panel, navigate to System. 
[[file:control_panel.png|800px]] 
2) Click on Advanced Systems Settings. In the Advanced tab, click Environment Variables. 
[[file:Adv_Sys_Set.png|800px]] 
3) Under System Variables, click on the Path Variable and select Edit. 
In the dialog box, under Variable value, add a semi-colon after the current text and then the full file path to the location of the TUFLOW executable. 
4) The linked model may be run by opening the .ief file within the Flood Modeller Interface and clicking Run. 
[[file:env_var.png|800px]]

===Batch File===
This method allows the user the flexibility to simulate models using different version of Flood Modeller and TUFLOW on the same computer. 
After setting up this method the model is run through a batch file. The benefit of this method is that it allows for the running of multiple simulations. Later versions of Flood Modeller have a Batch run facility which can be used for this purpose. 
1) Using a file explorer, navigate to the location of the TUFLOW executable files. Copy all of the files either within the 'w32' folder or 'w64' folder depending on whether the 32 bit or 64 bit version of Flood Modeller has been installed on the computer. 
[[file:tuflow_download_files.png|500px]] 
2) Navigate to the location on the local drive where Flood Modeller has been installed. In this case, it is ''C:\Program Files\Flood Modeller'' although your path may differ depending on your system and installation. 
Paste the copied TUFLOW files into the ''bin'' folder. If asked, copy and replace the files. 
3) To set up this method for multiple versions of Flood Modeller and TUFLOW: 
* Create an empty folder and appropriately rename it to denote the versions of Flood Modeller and TUFLOW.
* Copy the entirety of the 'bin' folder from the version of Flood Modeller that you wish to use.
* Paste this 'bin' folder inside the newly created folder. 
* Copy and paste in all TUFLOW executable files from the version that you wish to use inside the 'bin' folder. Remember to ensure either the 32-bit or 64-bit executables are copied. These need to be compatible with the version of Flood Modeller used.

[[File:Diff TF versions.JPG]]
 

To run linked Flood Modeller-TUFLOW models with this method, create a [http://wiki.tuflow.com/index.php?title=Run_TUFLOW_From_a_Batch-file batch file]. 
* In a text editor, paste the path to the Flood Modeller executable file (ISISf32.exe for single precision and ISISf32_DoubleP.exe for double precision) and the name of the .ief file. It may be necessary to include the filepath if the .ief file is not saved in the same folder as the batch file.
* Add the optional batch switch '-sd'. This is useful when running multiple simulations as it allows for the simulation to automatically shutdown on completion removing the need for any user intervention.
* Double click on the batch file in a file explorer to start the simulation.
[[File:FMT run batch.PNG|700px]] 

===Flood Modeller Batch Runs===
The Flood Modeller batch run functionality can be used to set up multiple batch runs of Flood Modeller-TUFLOW linked models. 
Refer to the following guidance document on the Flood Modeller [http://help.floodmodeller.com/floodmodeller/Batch_Simulations.htm website] for further information 

==Using TUFLOW Events and Scenarios when Running Linked Models ==
The Events and Scenarios feature within TUFLOW can be used when running linked models. Further information on this feature may be found within [http://www.tuflow.com/Download/Presentations/2012/2012%20Aust%20Workshops%20-%20TUFLOW%20Multiple%20Events%20and%20Scenarios.pdf this presentation] and in the [http://wiki.tuflow.com/index.php?title=Example_Models_Home_page TUFLOW example models]. 
 
Each unique Flood Modeller - TUFLOW simulation will still require its own .ief file. However, each of these .ief files may refer to the same TUFLOW .tcf control file. The TUFLOW Event or Scenario is specified within the 'Run Options' Section of the 'Links' tab within the .ief file. 

In the below example, the .ief file is named 'my_model_0100F_EXG_002.ief'. It references a TUFLOW .tcf file named 'my_model_~e1~_~s1~_002.tcf'. 
By adding '-e1 0100F -s1 EXG' to the 'Run Options' box, the '0100F' event and the 'EXG' scenario is selected by TUFLOW. 
 
[[File:FMT EventsScenarios.PNG]]

==Single vs Double Precision and 32-Bit vs 64-Bit==
A common error encountered when compiling linked Flood Modeller-TUFLOW models is inconsistency between versions. These errors will typically cause the model run to fail before the simulation has commenced. 
 
There are two factors to getting this correct: 
1) [http://www.computerhope.com/issues/ch001498.htm 32-Bit or 64-Bit] versions 
Both TUFLOW and Flood Modeller have both 32-Bit and 64-Bit versions. 
When installing Flood Modeller, the user has the option to selection which version to install. As TUFLOW does not require installation, the option of selecting 32-Bit or 64-Bit occurs when choosing which executable to simulate the model. It is important that the 32 or 64 bit versions match. This can be verified by viewing the Flood Modeller diagnositics file (.zzd) and the TUFLOW log file (.tlf). 

2) [http://www.tuflow.com/forum/index.php?/topic/821-single-precision-vs-double-precision/ Single vs Double Precision] 
Both TUFLOW and Flood Modeller have Single and Double precision versions. In short, these refer to the number of decimal points used when carrying out the calculations. Depending on the model, one version may be preferred over the other. CH2M have provided some further guidance on [https://www.floodmodeller.com/en-gb/news/articles/2013/6/double-precision-single-precision-and-batching-konrad-adams-isis-and-tuflow-specialist/ this page] . 
 

Whichever precision version is selected when simulation a linked Flood Modeller -TUFLOW model, it is important to ensure consistency between the packages. This can be verified by viewing the Flood Modeller diagnositics file (.zzd) and the TUFLOW log file (.tlf). 
[[File:Version_check.png]]

==Flood Modeller-TUFLOW HPC/Quadtree==

Flood Modeller is compatible with TUFLOW Classic, TUFLOW HPC and TUFLOW Quadtree. In order to use TUFLOW HPC, Flood Modeller 4.5 or later should be used. It is recommended that TUFLOW 2018-03-AE or later is used. This allows connectivity between Flood Modeller, TUFLOW HPC and TUFLOW 1D (aka ESTRY). The benefit of using TUFLOW HPC is to allow the 2D calculations to utilise GPU card technology for significant reductions in simulation time compared to TUFLOW Classic. The following section provides some benchmarking tests demonstrating this.

In order to use TUFLOW Quadtree, then Flood Modeller 4.6 is required, which should be used in conjunction with TUFLOW 2020-01-AB. Currently only HX connections from Flood Modeller to TUFLOW Quadtree are supported but SX connections will be supported in the 2020-10-AA release.

When using Flood Modeller and TUFLOW HPC/Quadtree, it is important that the Flood Modeller folder must contain copies of four TUFLOW files. This folder is called the “bin” folder and is located (by default) in “C:\Program Files\Flood Modeller”. The four required TUFLOW files are called:

* kernels_nSP.ptx
* kernels_nDP.ptx
* QPC_kernels_nDP.ptx
* QPC_kernels_nDP.ptx

The Flood Modeller installation includes versions of these files that should be compatible with the latest version of TUFLOW available at the time of the Flood Modeller release. If you need to use a later release of TUFLOW or if you find that the link in a Flood Modeller-TUFLOW coupled model is failing then browse to your TUFLOW engine folder, and copy the above four files and then paste them into your Flood Modeller bin folder (replacing the files there). 

== Troubleshooting ==

When switching between different build versions of TUFLOW it is possible to receive errors relating to the linking of the software. This is can be due to incorrect linking files being called when the link is established. Licencing appears to be fine but the TUFLOW initialisation fails with no specific error or warning. If a linked model is failing, with the last message in the .tlf being: 

<tt>Attempting </tt> <tt>to create XMDF file </tt> <tt>"\\Example_path\Model.xmdf"</tt> 

Navigate to the specific TUFLOW Build that is being used, locate the following files below and copy and paste these into the Flood Modeller Bin folder: 

*'''hdf5.dll'''
*'''hdf5_hl.dll'''

Should this error continue to happen it is recommended re-installing Flood Modeller or contacting support@tuflow.com attaching the .tlf and also informing which build versions of both software packages is being used.

==Flood Modeller-TUFLOW Benchmarking including TUFLOW HPC==

The following page provides information on some linked Flood Modeller-TUFLOW Benchmarking on different PC's utilising TUFLOW Classic, TUFLOW HPC on multiple CPU and TUFLOW HPC on a GPU Card.

[[Flood Modeller-TUFLOW Benchmarking|Flood Modeller-TUFLOW Benchmarking]].

Hardware Benchmarking - Results

2021-06-04T14:18:15Z

TuflowJoe: /* GPU Results */

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable"

! style="background-color:#005581; font-weight:bold; color:white;"| Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Runtime Combined (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|AMD Ryzen 9 5950X 16-Core Processor||3.4||32||3400||39.7||343.0||382.7||JG2
|-
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||2.9||16||2900||63.3||385.0||448.3||NCV
|-
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||4.1||16||4104||62.7||386.6||449.3||GZH
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|-
|}
 

=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||4.6||23.5||28.1||KW2
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||NVIDIA GeForce RTX 3070||8||5888||5.0||28.8||33.8||NCV
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||5.5||29.7||35.2||JG2
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||NVIDIA GeForce RTX 2060 SUPER (core 1647MHz, mem 1750MHz)||8||2176||5.6||33.4||39.1||GZH
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|Intel(R) Core(TM) i3-8100 CPU @ 3.60GHz ||NVIDIA Tesla K40c||12||2880||12.3||83.1||95.3||NWE
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|-
|}
 

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||158||1192.2||1350.2||CH3
|-
|Intel(R) Core(TM) i9-10900F CPU @ 3.70GHz ||NVIDIA GeForce RTX 3090||24||10496||162.3||1192.4||1354.7||LJA
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||178.9||1363.9||1542.8||KW2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||248.7||1928.6||2177.3||JG2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|-
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking_(2018-03-AA) | Back to TUFLOW Benchmarking]]
}}

Hardware Benchmarking - Results

2021-06-04T14:16:17Z

TuflowJoe: /* CPU Results */

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable"

! style="background-color:#005581; font-weight:bold; color:white;"| Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Runtime Combined (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|AMD Ryzen 9 5950X 16-Core Processor||3.4||32||3400||39.7||343.0||382.7||JG2
|-
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||2.9||16||2900||63.3||385.0||448.3||NCV
|-
|Intel(R) Core(TM) i5-10600K CPU @ 4.10GHz ||4.1||16||4104||62.7||386.6||449.3||GZH
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|-
|}
 

=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||4.6||23.5||28.1||KW2
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||NVIDIA GeForce RTX 3070||8||5888||5.0||28.8||33.8||NCV
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||5.5||29.7||35.2||JG2
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|Intel(R) Core(TM) i3-8100 CPU @ 3.60GHz ||NVIDIA Tesla K40c||12||2880||12.3||83.1||95.3||NWE
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|-
|}
 

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||158||1192.2||1350.2||CH3
|-
|Intel(R) Core(TM) i9-10900F CPU @ 3.70GHz ||NVIDIA GeForce RTX 3090||24||10496||162.3||1192.4||1354.7||LJA
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||178.9||1363.9||1542.8||KW2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||248.7||1928.6||2177.3||JG2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|-
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking_(2018-03-AA) | Back to TUFLOW Benchmarking]]
}}

Hardware Benchmarking - Results

2021-06-01T15:54:39Z

TuflowJoe: /* GPU Results */

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable"

! style="background-color:#005581; font-weight:bold; color:white;"| Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Runtime Combined (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|AMD Ryzen 9 5950X 16-Core Processor||3.4||32||3400||39.7||343.0||382.7||JG2
|-
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||2.9||16||2900||63.3||385.0||448.3||NCV
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|-
|}
 

=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||4.6||23.5||28.1||KW2
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||NVIDIA GeForce RTX 3070||8||5888||5.0||28.8||33.8||NCV
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||5.5||29.7||35.2||JG2
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|Intel(R) Core(TM) i3-8100 CPU @ 3.60GHz ||NVIDIA Tesla K40c||12||2880||12.3||83.1||95.3||NWE
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|-
|}
 

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||158||1192.2||1350.2||CH3
|-
|Intel(R) Core(TM) i9-10900F CPU @ 3.70GHz ||NVIDIA GeForce RTX 3090||24||10496||162.3||1192.4||1354.7||LJA
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||178.9||1363.9||1542.8||KW2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||248.7||1928.6||2177.3||JG2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|-
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking_(2018-03-AA) | Back to TUFLOW Benchmarking]]
}}

Hardware Benchmarking - Results

2021-03-15T13:51:12Z

TuflowJoe:

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable"

! style="background-color:#005581; font-weight:bold; color:white;"| Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Runtime Combined (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|AMD Ryzen 9 5950X 16-Core Processor||3.4||32||3400||39.7||343.0||382.7||JG2
|-
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||2.9||16||2900||63.3||385.0||448.3||NCV
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|-
|}
 

=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||4.6||23.5||28.1||KW2
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||NVIDIA GeForce RTX 3070||8||5888||5.0||28.8||33.8||NCV
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||5.5||29.7||35.2||JG2
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|-
|}
 

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||158||1192.2||1350.2||CH3
|-
|Intel(R) Core(TM) i9-10900F CPU @ 3.70GHz ||NVIDIA GeForce RTX 3090||24||10496||162.3||1192.4||1354.7||LJA
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||178.9||1363.9||1542.8||KW2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||248.7||1928.6||2177.3||JG2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|-
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking_(2018-03-AA) | Back to TUFLOW Benchmarking]]
}}

Hardware Benchmarking - Results

2021-01-05T14:30:47Z

TuflowJoe: /* High End GPU Results */

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable"

! style="background-color:#005581; font-weight:bold; color:white;"| Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Runtime Combined (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|AMD Ryzen 9 5950X 16-Core Processor||3.4||32||3400||39.7||343.0||382.7||JG2
|-
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||2.9||16||2900||63.3||385.0||448.3||NCV
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|-
|}
 

=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||4.6||23.5||28.1||KW2
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||NVIDIA GeForce RTX 3070||8||5888||5.0||28.8||33.8||NCV
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||5.5||29.7||35.2||JG2
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|-
|}
 

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||158||1192.2||1350.2||CH3
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||178.9||1363.9||1542.8||KW2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||248.7||1928.6||2177.3||JG2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|-
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking_(2018-03-AA) | Back to TUFLOW Benchmarking]]
}}

Hardware Benchmarking - Results

2021-01-05T12:42:03Z

TuflowJoe:

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable"

! style="background-color:#005581; font-weight:bold; color:white;"| Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Runtime Combined (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|AMD Ryzen 9 5950X 16-Core Processor||3.4||32||3400||39.7||343.0||382.7||JG2
|-
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||2.9||16||2900||63.3||385.0||448.3||NCV
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|-
|}
 

=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||4.6||23.5||28.1||KW2
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||NVIDIA GeForce RTX 3070||8||5888||5.0||28.8||33.8||NCV
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||8||5888||5.5||29.7||35.2||JG2
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|-
|}
 

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||158||1192.2||1350.2||CH3
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3080||10||8704||178.9||1363.9||1542.8||KW2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||24||5888||248.7||1928.6||2177.3||JG2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|-
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking_(2018-03-AA) | Back to TUFLOW Benchmarking]]
}}

Hardware Benchmarking - Results

2021-01-04T14:25:45Z

TuflowJoe:

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable"

! style="background-color:#005581; font-weight:bold; color:white;"| Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Runtime Combined (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|AMD Ryzen 9 5950X 16-Core Processor||3.4||32||3400||39.7||343.0||382.7||JG2
|-
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||2.9||16||2900||63.3||385.0||448.3||NCV
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|-
|}
 

=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz ||NVIDIA GeForce RTX 3070||16||5888||5.0||28.8||33.8||NCV
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||24||5888||5.5||29.7||35.2||JG2
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|-
|}
 

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||158||1192.2||1350.2||CH3
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||24||5888||248.7||1928.6||2177.3||JG2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|-
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking_(2018-03-AA) | Back to TUFLOW Benchmarking]]
}}

Hardware Benchmarking - Results

2021-01-04T14:18:14Z

TuflowJoe:

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable"

! style="background-color:#005581; font-weight:bold; color:white;"| Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Runtime Combined (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|AMD Ryzen 9 5950X 16-Core Processor||3.4||32||3400||39.7||343.0||382.7||JG2
|-
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|-
|}
 

=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||24||5888||5.5||29.7||35.2||JG2
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|-
|}
 

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||158||1192.2||1350.2||CH3
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|AMD Ryzen 9 5950X 16-Core Processor ||NVIDIA GeForce RTX 3070||24||5888||248.7||1928.6||2177.3||JG2
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|-
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking_(2018-03-AA) | Back to TUFLOW Benchmarking]]
}}

1D Pits

2020-11-17T14:39:03Z

TuflowJoe:

=Introduction=
There are predominantly two types of stormwater pits (drains/gullies) used as inlets to collect overland runoff and transfer that water to the underlying drainage/culvert/pipe network;
* Grated inlets
* Kerb Inlets (side entry pits / lintel inlets).
This page of the Wiki describes how pit inlet data is incorporated into a TUFLOW model.

= Pit Inlet Types =
{|class="wikitable"
|-
| [[File:Gully pit.jpg|thumb|none|200px|Grate (London, UK)]] || width="300pt"|Grates, also known as Gully Pits, are common in the United Kingdom and are generally a square grate on top of a circular chamber and a riser outlet. The outlet will then feed into a larger culvert that forms part of the larger urban drainage network. || [[File:Side_Entry_pit.jpg|thumb|none|300px|Kerb Inlet (http://www.lgam.info/side-entry-pit)]]|| width="300pt"|Kerb inlets, also know as side entry pits or lintels are common in Australia. The pit chamber can vary depending on overall depth, length and the addition of any haunched riser units.
|}
Pit inlet inflow information is defined within TUFLOW via a user defined Pit Inlet Database and associated pit inlet curves. This approach allows for unlimited flexibility. Any pit design or configuration can be incorporated into a TUFLOW model if the inlet depth-discharge relationship is known.

= Pit Inlet Data Sources =
Pit inlet depth-discharge data can be obtained from a variety of sources. The most common typically being from suppliers or local agencies who enforce consistent design standards within their jurisdiction. For demonstration purposes, examples from Sutherland Shire Council and Brisbane City Council are provided below.

== Sutherland Shire Council ==
The Sutherland Shire Council Urban Drainage Manual (1992) includes summary tables and graphs documenting pit grate and lintel capacity information (derived from Department of Main Roads testing). The guidelines are compatible with the standard pit grate and lintel design shown below: 
[[File: Pit_Inlet_Curves_SSC000.JPG|700px]] 
The capacity of a pit depends on three factors:
* The clear opening area of the grate
* The depth of water ponding over the grate
* The length of kerb inlet (lintel) opening
The following graphs summarise grate and lintel discharge estimates for a range of water depths and blockage factors, derived from the Sutherland Shire Council design standards. 
[[File: Pit_Inlet_Curves_SSC001.JPG|700px]]
[[File: Pit_Inlet_Curves_SSC002.JPG|700px]] 

The above graphs estimate unit length and area flow estimates. These unit values can be multiplied by real pit dimensions to define at site depth-discharge characteristics.

TUFLOW modelling requires the derivation of a unique depth-discharge curve for each pit type within the modelled area. An example is provided below for a single pit location. 

[[File: Pit_Inlet_Curves_SSC003.JPG|700px]]

== Brisbane City Council==
The pit inlet curve examples below originate from Brisbane City Council 8000 series standard drawings: https://www.brisbane.qld.gov.au/planning-building/planning-guidelines-tools/planning-guidelines/standard-drawings 
[[File: BCC_BSD-8077.JPG|border|700px]]
[[File: BCC_BSD-8051.JPG|border|700px]] 
[[File: BCC_BSD-8082.JPG|border|700px]]
[[File: BCC_BSD-8052.JPG|border|700px]] 

== South Australian Road Stormwater Drainage Inlets: Hydraulic Study (University of South Australia)==
The Urban Water Resources Centre (UWRC) at the University of South Australia conducted a comprehensive set of hydraulic studies, examining performance of the most common roads’ stormwater drainage inlets in use in South Australia. The study was carried out using the Centre's unique full-scale road surface drainage test rig. Links to the pipe inlets curves are provided in the following link and sections below: [https://www.unisa.edu.au/research/scarce-resources-and-circular-economy/australian-flow-management-group/stormwater-drainage-hydraulic-study/ Hydraulic Study (University of South Australia)]
=== Transport South Australia ===
[https://www.unisa.edu.au/research/scarce-resources-and-circular-economy/australian-flow-management-group/stormwater-drainage-hydraulic-study/transport-sa/ Transport South Australia Pit Inlet Curves]
=== City of Adelaide ===
[https://www.unisa.edu.au/research/scarce-resources-and-circular-economy/australian-flow-management-group/stormwater-drainage-hydraulic-study/city-of-adelaide/ City of Adelaide Pit Inlet Curves]
=== City of Campbelltown ===
[https://www.unisa.edu.au/research/scarce-resources-and-circular-economy/australian-flow-management-group/stormwater-drainage-hydraulic-study/city-of-campbeltown/ City of Campbelltown Pit Inlet Curves]
=== City of Charles Sturt ===
[https://www.unisa.edu.au/research/scarce-resources-and-circular-economy/australian-flow-management-group/stormwater-drainage-hydraulic-study/city-of-charles-sturt/ City of Charles Sturt Pit Inlet Curves]
=== City of Onkaparinga ===
[https://www.unisa.edu.au/research/scarce-resources-and-circular-economy/australian-flow-management-group/stormwater-drainage-hydraulic-study/city-of-onkaparinga/ City of Onkaparinga Pit Inlet Curves]
=== City of Playford ===
[https://www.unisa.edu.au/research/scarce-resources-and-circular-economy/australian-flow-management-group/stormwater-drainage-hydraulic-study/city-of-playford/ City of Playford Pit Inlet Curves]
=== City of Port Adelaide / Enfield ===
[https://www.unisa.edu.au/research/scarce-resources-and-circular-economy/australian-flow-management-group/stormwater-drainage-hydraulic-study/city-of-port-adelaideenfield/ City of Port Adelaide / Enfield Pit Inlet Curves]
=== City of Marion ===
[https://www.unisa.edu.au/research/scarce-resources-and-circular-economy/australian-flow-management-group/stormwater-drainage-hydraulic-study/city-of-marion/ City of Marion Pit Inlet Curves]
=== City of Mitcham ===
[https://www.unisa.edu.au/research/scarce-resources-and-circular-economy/australian-flow-management-group/stormwater-drainage-hydraulic-study/city-of-mitcham/ City of Mitcham Pit Inlet Curves]
=== City of Salisbury ===
[https://www.unisa.edu.au/research/scarce-resources-and-circular-economy/australian-flow-management-group/stormwater-drainage-hydraulic-study/city-of-salisbury/ City of Salisbury Pit Inlet Curves]
=== City of Tea Tree Gully ===
[https://www.unisa.edu.au/research/scarce-resources-and-circular-economy/australian-flow-management-group/stormwater-drainage-hydraulic-study/city-of-tea-tree-gully/ City of Tea Tree Gully Pit Inlet Curves]
=== City of West Torrens ===
[https://www.unisa.edu.au/research/scarce-resources-and-circular-economy/australian-flow-management-group/stormwater-drainage-hydraulic-study/city-of-west-torrens/ City of West Torrens Pit Inlet Curves]

== How to Translate On-Grade Approach Flow / Pit Capture Flow Curves to Depth / Discharge Curves for use in TUFLOW ==
Manning's equation calculations can be used to convert on-grade approach flow information to a water depth at the pit. After manually calculating the depth to flow relationship the user simply needs to associate these water depth values with the pit capture flow information typically included in on grade pit inlet curve datasets.

=TUFLOW Model Inputs=
The steps required to represent pit inlet information within a TUFLOW model is summaried below:
<ol>
<li> Import an empty 1d_nwk GIS file from the TUFLOW file template folder (model\gis\empty).
<li> Digitise points to represent pit locations. The points should be snapped to the start or end of 1d_nwk line features that define the underground pipe network.
<li> Update the field attributes for each Pit point. Refer to 5.12.2 of the 2016 TUFLOW Manual. The most commonly used attributes are:
* Type = Q (C, R and W are also options if pit inlets based on structure dimension is required instead of using a Pit Inlet Database approach).
* US_Invert = Used to specify the ground elevation of the pit. If Conn_1D_2D is set to “SXL”, US_Invert is used as the amount by which to lower the 2D cell and the pit channel invert is set to this level.
* DS_Invert = The bottom elevation of the pit. This input can also be used to set the upstream and downstream inverts of connected pipes/channels.
* Inlet_Type = For Q pit channels, the name of a pit inlet type in the Pit Inlet Database. Multiple Pits can use the same Inlet_Type ID if they have the same design dimensions.
* Conn_1D_2D = SXL can be specified to connect the 1D pit or node to the 2D domain and lower the 2D cell by the amount of the US_Invert attribute. The invert of the pit channel is set to the lowered 2D cell level. This is useful to help trap the water into the pit as it flows overland in the 2D domain. This feature works well in combination with the new Read GIS SA PITS option.
<li> Update the Estry Control File (*.ecf) to include reference to the new 1d_nwk GIS file. 
<tt>Read GIS Network</tt> <tt>==</tt><tt>..\model\mi\Estry\1d_nwk_****.MIF</tt> 
<li> Create a Pit Inlet Database csv file. This file lists each type of pit (matching the names listed in the 1d_nwk "Inlet_Type" field). It also provides reference to the depth-discharge curve information. 
[[File: Pit_Inlet_Dbase_000.JPG]]
<li> Create the Source file referenced in the Pit Inlet Database. The source file defines the depth-discharge information for each pit (determined from the relevant design standards). The column headers must match the entries in the Pit Inlet Database. 
[[File: Pit_Inlet_Dbase_001.JPG]]
<li> Update the Estry Control File (*.ecf) to include reference to the Pit Inlet Database. 
<tt>Pit Inlet Database</tt> <tt>==</tt><tt>..\pit_dbase\EG_pit_dbase.csv</tt> 
</ol>

An example model including Pits is available for download: http://wiki.tuflow.com/index.php?title=Example_Models_Home_page

==Pit Search Distance==
Some agency pit datasets may not have all pit point features snapped to the associated pipe line features. The *.ecf command "Pit Search Distance" can be useful in this instance. It automatically connects floating nodes to the 1D pipe network where connectors are not snapped to channel ends: 

<tt>Pit Search Distance</tt> <tt>==</tt><tt>xxx</tt> 
<tt>Read GIS Network</tt> <tt>==</tt><tt>..\model\mi\1d_nwke_*****.MIF</tt> 
 
The order of the "Pit Search Distance" command is important as it can be repeated multiple times with different values that are assigned to the 1d_nwke(s) below the ''Pit Search Distance'' command. There is an example on the TUFLOW Forum [http://www.tuflow.com/forum/index.php?/topic/1232-pit-search-distance-not-working/ here] that describes the setup of the commands within the .ecf. 
 
To check if the ''Pit Search Distance'' is working as expected, import the [[Check_Files_1d_nwk_C | *_nwk_C_check]] file to visually see if the pits are automatically connecting to a culvert. The image below is an example of the *_nwk_C_check file and the connections TUFLOW has made to each pit. 
[[File:Pit_search_distance_check.JPG|border|500px]]

 
 
Any further questions please email TUFLOW support: [mailto:support@tuflow.com?Subject=TUFLOW%201D%20pits%20help support@tuflow.com]
 
Back to [[TUFLOW_1D_Hydraulic_Structures | 1D Hydraulic Structures]]

1D Pits

2020-11-17T14:34:43Z

TuflowJoe:

=Introduction=
There are predominantly two types of stormwater pits (drains/gullies) used as inlets to collect overland runoff and transfer that water to the underlying drainage/culvert/pipe network;
* Grated inlets
* Kerb Inlets (side entry pits / lintel inlets).
This page of the Wiki describes how pit inlet data is incorporated into a TUFLOW model.

= Pit Inlet Types =
{|class="wikitable"
|-
| [[File:Gully pit.jpg|thumb|none|200px|Grate (London, UK)]] || width="300pt"|Grates, also known as Gully Pits, are common in the United Kingdom and are generally a square grate on top of a circular chamber and a riser outlet. The outlet will then feed into a larger culvert that forms part of the larger urban drainage network. || [[File:Side_Entry_pit.jpg|thumb|none|300px|Kerb Inlet (http://www.lgam.info/side-entry-pit)]]|| width="300pt"|Kerb inlets, also know as side entry pits or lintels are common in Australia. The pit chamber can vary depending on overall depth, length and the addition of any haunched riser units.
|}
Pit inlet inflow information is defined within TUFLOW via a user defined Pit Inlet Database and associated pit inlet curves. This approach allows for unlimited flexibility. Any pit design or configuration can be incorporated into a TUFLOW model if the inlet depth-discharge relationship is known.

= Pit Inlet Data Sources =
Pit inlet depth-discharge data can be obtained from a variety of sources. The most common typically being from suppliers or local agencies who enforce consistent design standards within their jurisdiction. For demonstration purposes, examples from Sutherland Shire Council and Brisbane City Council are provided below.

== Sutherland Shire Council ==
The Sutherland Shire Council Urban Drainage Manual (1992) includes summary tables and graphs documenting pit grate and lintel capacity information (derived from Department of Main Roads testing). The guidelines are compatible with the standard pit grate and lintel design shown below: 
[[File: Pit_Inlet_Curves_SSC000.JPG|700px]] 
The capacity of a pit depends on three factors:
* The clear opening area of the grate
* The depth of water ponding over the grate
* The length of kerb inlet (lintel) opening
The following graphs summarise grate and lintel discharge estimates for a range of water depths and blockage factors, derived from the Sutherland Shire Council design standards. 
[[File: Pit_Inlet_Curves_SSC001.JPG|700px]]
[[File: Pit_Inlet_Curves_SSC002.JPG|700px]] 

The above graphs estimate unit length and area flow estimates. These unit values can be multiplied by real pit dimensions to define at site depth-discharge characteristics.

TUFLOW modelling requires the derivation of a unique depth-discharge curve for each pit type within the modelled area. An example is provided below for a single pit location. 

[[File: Pit_Inlet_Curves_SSC003.JPG|700px]]

== Brisbane City Council==
The pit inlet curve examples below originate from Brisbane City Council 8000 series standard drawings: https://www.brisbane.qld.gov.au/planning-building/planning-guidelines-tools/planning-guidelines/standard-drawings 
[[File: BCC_BSD-8077.JPG|border|700px]]
[[File: BCC_BSD-8051.JPG|border|700px]] 
[[File: BCC_BSD-8082.JPG|border|700px]]
[[File: BCC_BSD-8052.JPG|border|700px]] 

== South Australian Road Stormwater Drainage Inlets: Hydraulic Study (University of South Australia)==
The Urban Water Resources Centre (UWRC) at the University of South Australia conducted a comprehensive set of hydraulic studies, examining performance of the most common roads’ stormwater drainage inlets in use in South Australia. The study was carried out using the Centre's unique full-scale road surface drainage test rig. Links to the pipe inlets curves are provided in the following link and sections below: [https://www.unisa.edu.au/research/scarce-resources-and-circular-economy/australian-flow-management-group/stormwater-drainage-hydraulic-study/ Hydraulic Study (University of South Australia)]
=== Transport South Australia ===
[https://www.unisa.edu.au/research/scarce-resources-and-circular-economy/australian-flow-management-group/stormwater-drainage-hydraulic-study/transport-sa/ Transport South Australia Pit Inlet Curves]
=== City of Adelaide ===
[https://www.unisa.edu.au/research/scarce-resources-and-circular-economy/australian-flow-management-group/stormwater-drainage-hydraulic-study/city-of-adelaide/ City of Adelaide Pit Inlet Curves]
=== City of Campbeltown ===
[https://www.unisa.edu.au/research/scarce-resources-and-circular-economy/australian-flow-management-group/stormwater-drainage-hydraulic-study/city-of-campbeltown/ City of Campbeltown Pit Inlet Curves]
=== City of Charles Sturt ===
[https://www.unisa.edu.au/research/scarce-resources-and-circular-economy/australian-flow-management-group/stormwater-drainage-hydraulic-study/city-of-charles-sturt/ City of Charles Sturt Pit Inlet Curves]
=== City of Onkaparinga ===
[https://www.unisa.edu.au/research/scarce-resources-and-circular-economy/australian-flow-management-group/stormwater-drainage-hydraulic-study/city-of-onkaparinga/ City of Onkaparinga Pit Inlet Curves]
=== City of Playford ===
[https://www.unisa.edu.au/research/scarce-resources-and-circular-economy/australian-flow-management-group/stormwater-drainage-hydraulic-study/city-of-playford/ City of Playford Pit Inlet Curves]
=== City of Port Adelaide / Enfield ===
[https://www.unisa.edu.au/research/scarce-resources-and-circular-economy/australian-flow-management-group/stormwater-drainage-hydraulic-study/city-of-port-adelaideenfield/ City of Port Adelaide / Enfield Pit Inlet Curves]
=== City of Marion ===
[https://www.unisa.edu.au/research/scarce-resources-and-circular-economy/australian-flow-management-group/stormwater-drainage-hydraulic-study/city-of-marion/ City of Marion Pit Inlet Curves]
=== City of Mitcham ===
[https://www.unisa.edu.au/research/scarce-resources-and-circular-economy/australian-flow-management-group/stormwater-drainage-hydraulic-study/city-of-mitcham/ City of Mitcham Pit Inlet Curves]
=== City of Salisbury ===
[https://www.unisa.edu.au/research/scarce-resources-and-circular-economy/australian-flow-management-group/stormwater-drainage-hydraulic-study/city-of-salisbury/ City of Salisbury Pit Inlet Curves]
=== City of Tea Tree Gully ===
[https://www.unisa.edu.au/research/scarce-resources-and-circular-economy/australian-flow-management-group/stormwater-drainage-hydraulic-study/city-of-tea-tree-gully/ City of Tea Tree Gully Pit Inlet Curves]
=== City of West Torrens ===
[https://www.unisa.edu.au/research/scarce-resources-and-circular-economy/australian-flow-management-group/stormwater-drainage-hydraulic-study/city-of-west-torrens/ City of West Torrens Pit Inlet Curves]

== How to Translate On-Grade Approach Flow / Pit Capture Flow Curves to Depth / Discharge Curves for use in TUFLOW ==
Manning's equation calculations can be used to convert on-grade approach flow information to a water depth at the pit. After manually calculating the depth to flow relationship the user simply needs to associate these water depth values with the pit capture flow information typically included in on grade pit inlet curve datasets.

=TUFLOW Model Inputs=
The steps required to represent pit inlet information within a TUFLOW model is summaried below:
<ol>
<li> Import an empty 1d_nwk GIS file from the TUFLOW file template folder (model\gis\empty).
<li> Digitise points to represent pit locations. The points should be snapped to the start or end of 1d_nwk line features that define the underground pipe network.
<li> Update the field attributes for each Pit point. Refer to 5.12.2 of the 2016 TUFLOW Manual. The most commonly used attributes are:
* Type = Q (C, R and W are also options if pit inlets based on structure dimension is required instead of using a Pit Inlet Database approach).
* US_Invert = Used to specify the ground elevation of the pit. If Conn_1D_2D is set to “SXL”, US_Invert is used as the amount by which to lower the 2D cell and the pit channel invert is set to this level.
* DS_Invert = The bottom elevation of the pit. This input can also be used to set the upstream and downstream inverts of connected pipes/channels.
* Inlet_Type = For Q pit channels, the name of a pit inlet type in the Pit Inlet Database. Multiple Pits can use the same Inlet_Type ID if they have the same design dimensions.
* Conn_1D_2D = SXL can be specified to connect the 1D pit or node to the 2D domain and lower the 2D cell by the amount of the US_Invert attribute. The invert of the pit channel is set to the lowered 2D cell level. This is useful to help trap the water into the pit as it flows overland in the 2D domain. This feature works well in combination with the new Read GIS SA PITS option.
<li> Update the Estry Control File (*.ecf) to include reference to the new 1d_nwk GIS file. 
<tt>Read GIS Network</tt> <tt>==</tt><tt>..\model\mi\Estry\1d_nwk_****.MIF</tt> 
<li> Create a Pit Inlet Database csv file. This file lists each type of pit (matching the names listed in the 1d_nwk "Inlet_Type" field). It also provides reference to the depth-discharge curve information. 
[[File: Pit_Inlet_Dbase_000.JPG]]
<li> Create the Source file referenced in the Pit Inlet Database. The source file defines the depth-discharge information for each pit (determined from the relevant design standards). The column headers must match the entries in the Pit Inlet Database. 
[[File: Pit_Inlet_Dbase_001.JPG]]
<li> Update the Estry Control File (*.ecf) to include reference to the Pit Inlet Database. 
<tt>Pit Inlet Database</tt> <tt>==</tt><tt>..\pit_dbase\EG_pit_dbase.csv</tt> 
</ol>

An example model including Pits is available for download: http://wiki.tuflow.com/index.php?title=Example_Models_Home_page

==Pit Search Distance==
Some agency pit datasets may not have all pit point features snapped to the associated pipe line features. The *.ecf command "Pit Search Distance" can be useful in this instance. It automatically connects floating nodes to the 1D pipe network where connectors are not snapped to channel ends: 

<tt>Pit Search Distance</tt> <tt>==</tt><tt>xxx</tt> 
<tt>Read GIS Network</tt> <tt>==</tt><tt>..\model\mi\1d_nwke_*****.MIF</tt> 
 
The order of the "Pit Search Distance" command is important as it can be repeated multiple times with different values that are assigned to the 1d_nwke(s) below the ''Pit Search Distance'' command. There is an example on the TUFLOW Forum [http://www.tuflow.com/forum/index.php?/topic/1232-pit-search-distance-not-working/ here] that describes the setup of the commands within the .ecf. 
 
To check if the ''Pit Search Distance'' is working as expected, import the [[Check_Files_1d_nwk_C | *_nwk_C_check]] file to visually see if the pits are automatically connecting to a culvert. The image below is an example of the *_nwk_C_check file and the connections TUFLOW has made to each pit. 
[[File:Pit_search_distance_check.JPG|border|500px]]

 
 
Any further questions please email TUFLOW support: [mailto:support@tuflow.com?Subject=TUFLOW%201D%20pits%20help support@tuflow.com]
 
Back to [[TUFLOW_1D_Hydraulic_Structures | 1D Hydraulic Structures]]

Hardware Benchmarking - Results

2020-10-14T12:59:11Z

TuflowJoe: /* High End GPU Results */

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable"

! style="background-color:#005581; font-weight:bold; color:white;"| Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Runtime Combined (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|-
|}
 

=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|-
|}
 

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||158||1192.2||1350.2||CH3
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|-
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking_(2018-03-AA) | Back to TUFLOW Benchmarking]]
}}

Hardware Benchmarking - Results

2020-10-14T12:56:29Z

TuflowJoe: /* GPU Results */

=CPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m Classic and HPC CPU runtimes, with the fastest computers at the top of the table.
 
'''Runtimes for CPU benchmarks'''
{| align="center" class="wikitable"

! style="background-color:#005581; font-weight:bold; color:white;"| Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Processor Frequency (GHz)**
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM size (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | RAM frequency (MHz)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Classic 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | HPC CPU 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=10% | Runtime Combined (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz||3.7||128||3200||56.9||173.8||230.7||CH1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz)||(5.1)||16||4000||58.2||179.9||238.1||RRB
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||3000||55.7||186.2||241.9||RH1
|-
|AMD Ryzen 9 3900X 12-Core Processor||3.8||64||3200||45.9||203.1||249.0||CH3
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz||3.1||32||2666||61.7||190.0||251.7||CB1
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz||3.6||32||2133||61.4||190.4||251.8||RH2
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||32||2933||62.1||192.8||254.9||DS2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||71.2||184.3||255.5||ABA
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||62.1||193.7||255.8||MA2
|-
|AMD Ryzen Threadripper 3970X 32-Core Processor||3.7||256||2400||49.5||212.1||261.6||CH2
|-
|AMD Ryzen 9 3950X 16-Core Processor||3.5||128||2800||55.9||210.0||265.9||TRO
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz||3.7||64||2133||67.0||202.5||269.5||RL1
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz||3.5||128||2133||72.7||202.1||274.8||MBL
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||67.5||208.6||276.1||SKI
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||68.5||208.8||277.3||PM2
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz||3.6||32||2666||66.8||211.6||278.1||MA1
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz||3.2||16||2667||68.2||211.8||280.0||CR2
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||68.0||216.0||284.0||RL3
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||2.7||32||2667||67.7||219.1||286.8||RL2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz||4.2||64||2133||70.5||218.8||289.3||PM1
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||2.9||16||2667||77.5||212.6||290.1||KTC
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||3.7||32||2933||75.2||219.8||295||BL-DT01001
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor||3.5||128||2666||67.9||230.8||298.7||JGR
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||32||2400||80.5||221.9||302.4||JM1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz||3.6||16||2400||72.8||230.6||303.4||RSH
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||85.4||218.8||304.2||JPI
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz||3.6||64||2666||83.7||225.2||308.9||HNM
|-
|Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz||4.0||32||2400||91.2||223.3||314.5||BRD
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz||3.7||64||2400||84.5||236.6||321.2||DS1
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz||3.2||128||2133||83.4||243.1||326.5||BLK
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz||3.4||128||2400||85.3||247.7||332.9||615
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2400||106.3||226.7||333.0||MRT
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||16||2133||80.7||253.4||334.1||VHD
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz||2.1||64||2666||83.9||251.2||335.1||AR2
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor||3.4||16||2666||81.9||277.3||359.2||CEV
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz||3.0||32||2400||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz||3.4||64||2133||82.3||289.8||372.1||JIW
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz||3.4||32||1666||108.9||270.0||378.9||AR1
|-
|Intel(R) Core(TM) i7-4810MQ CPU @ 2.80GHz||2.8||32||1600||119.7||280.8||400.5||SBC
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz||3.3||64||2133||118.2||287.8||406.0||ZDO
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz||2.7||16||2133||90.4||321.7||412.1||EAS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||3.3||16||2133||84.8||336.9||421.7||JM2
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz||3.0||64||2133||100.3||323||423.3||MON
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz||3.4||32||1600||126.4||299.7||426.1||MAV
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||2.6||24||1600||100.5||378.6||479.1||CDH
|-
|Intel(R) Xeon(R) CPU E5-1650 0 @ 3.20GHz||3.2||32||1600||143.2||343.1||486.3||MPR
|-
|Intel(R) Core(TM) i5-3470 CPU @ 3.20GHz||3.2||8||1600||142.0||349.4||491.4||CR1
|-
|Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz||3.3||16||N/A||196.8||331.5||528.3||Private Cloud
|-
|Intel(R) Core(TM) i7-4712HQ CPU @ 2.30GHz (Laptop)||2.3||8||1600||145.2||414.4||559.6||MNG
|-
|Intel(R) Xeon(R) CPU X5680 @ 3.33GHz||3.33||72||1333||165.7||400.6||566.3||WTM
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||2.4||8||1600||137.8||795.0||932.8||HMM
|-
|}
 

=GPU Results=
The following table summarises the runtimes for a range of computers. More will be added when additional results are obtained. The table is ordered based on the combined 20m and 10m runtimes with the fastest computers at the top of the table.
 
The HPC GPU benchmark only uses a single GPU card. TUFLOW HPC GPU can be run across multiple NVIDIA GPU devices. However, the benefits of these are typically more noticeable for larger models with more than 1 million cells.
 
'''Runtimes for GPU benchmarks'''
{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 20m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 10m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||3.4||18.5||21.9||FM-NODE: Tesla V100
|-
|AMD Ryzen 9 3900X 12-Core Processor ||NVIDIA GeForce RTX 3090||24||10496||4.4||20.4||24.8||CH3
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 3080||10||8704||4.7||24.0||28.7||CPM
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||4.7||25.7||30.4||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||5.7||25.2||30.9||JGR
|-
|AMD Ryzen 9 3950X 16-Core Processor ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.3||26.4||31.7||TRO
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.4||27.4||32.8||PY2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||27.7||33.4||VLD
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.5||28.4||33.9||RL1
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||5.5||28.7||34.2||RRB
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.8||28.8||34.6||MA1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.7||29.2||34.9||MA2
|-
|Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||5.6||29.9||35.5||MBL
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||6.4||29.5||35.9||JPI
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P100||16||3584||6.1||30.8||36.9||Google Cloud: Tesla P100
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||5.7||31.9||37.6||RH2
|-
|Intel(R) Core(TM) i7-6700k @ 4.00GHz ||NVIDIA GeForce RTX 2080||8||2944||6.0||31.9||37.9||ANK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||5.6||34.3||40.7||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||6.9||34.6||41.6||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.4||35.8||42.2||JS1
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080||8||2944||6.5||36.0||42.5||ABA
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.1||37.1||43.2||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||6.5||37.4||43.8||PM1
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 2070||8||2304||7.4||38.8||46.2||MMR
|-
|Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz ||NVIDIA Quadro RTX 4000||8||2304||6.6||39.8||46.4||CB1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.8||39.1||46.9||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||8.0||39.3||47.3||RCD
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||7.6||41.1||48.7||HNM
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (Laptop) ||NVIDIA GeForce RTX 2070||8||2304||7.7||42.8||50.5||ERX
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||8.0||47.4||55.3||BLK
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla T4||16||2560||7.3||48.3||55.6||FM-NODE: Tesla T4
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz ||NVIDIA GeForce GTX 1080||8||2560||8.5||48.9||57.3||JM1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||8.2||51.9||60.0||SKI
|-
|Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz ||NVIDIA Quadro P5000||16||2560||9.6||51.8||61.4||AR2
|-
|Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz ||NVIDIA GeForce GTX 1070||8||1920||8.9||54.9||63.8||PY1
|-
|Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz ||NVIDIA GeForce GTX 1070||8||1920||10.3||59.3||69.5||DS1
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA Quadro P4000||8||1792||10.0||62.3||72.3||DS2
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla P4||8||2560||10.4||69.0||79.4||Google Cloud: Tesla P4
|-
|Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz ||NVIDIA GeForce GTX 980||4||2048||11.8||70.6||82.3||ZDO
|-
|Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz ||NVIDIA GeForce GTX TITAN Black||6||2880||13.1||76.1||89.2||AR1
|-
|Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz ||NVIDIA GeForce GTX 1060||6||1280||13.0||77.1||90.1||RSH
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla K80||12||2496||11.3||83.5||94.8||Google Cloud: Tesla K80
|-
|Intel(R) Core(TM) i7-5960X CPU @ 3.00GHz ||NVIDIA GeForce GTX 980||4||2048||17.5||84.2||101.7||MRT
|-
|Intel(R) Xeon(R) W-2255 CPU @ 3.70GHz||NVIDIA Quadro P2000||5||1024||14.2||96||110.2||BL-DT01001
|-
|Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz ||NVIDIA Quadro P2000||5||1024||15.8||100.1||115.9||CR2
|-
|Intel(R) Xeon(R) CPU E3-1240 V2 @ 3.40GHz ||NVIDIA GeForce GTX 690||2||3072||18.4||114.4||132.8||MAV
|-
|AMD Ryzen Threadripper 1950X 16-Core Processor ||NVIDIA GeForce GTX 960||4||1024||18.6||123.3||141.6||CEV
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA GeForce GTX 960||4||1024||19.9||127.2||147.1||VHD
|-
|Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz||NVIDIA GeForce GTX 1050 Ti||4||768||21.1||133.8||154.9||MJS
|-
|Intel(R) Core(TM) i9-7900X CPU @ 3.30GHz||NVIDIA GeForce GTX 1050||2||640||20.6||139.1||159.7||JM2
|-
|Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz||NVIDIA Quadro P2000||5||1024||22.1||142.5||164.6||KTC
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz||NVIDIA Quadro P2000||5||1024||23.0||149.3||172.3||RL2
|-
|Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz||NVIDIA Quadro M2200||4||1024||23.2||198.7||222.0||GYB
|-
|Intel(R) Xeon(R) E-2176M CPU @ 2.70GHz ||NVIDIA Quadro P1000||4||640||30.3||203.7||234.0||RL3
|-
|Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz ||NVIDIA Quadro K2200||4||640||32.5||211.3||243.8||JIW
|-
|Intel(R) Xeon(R) CPU E3-1505M v6 @ 3.00GHz ||NVIDIA Quadro M1200||4||640||84.9||278.4||363.3||GHY
|-
|Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz ||NVIDIA GeForce GTX 940MX||2||384||65.6||479.0||544.6||EAS
|-
|Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz||NVIDIA GeForce 840M||2||384||70.6||526.3||595.9||CDH
|-
|Intel(R) Core(TM) i7-4700MQ CPU @ 2.40GHz (Laptop)||NVIDIA GeForce GT 740M||2||384||102.3||694.0||796.3||HMM
|-
|}
 

=High End GPU Results=
A number of additional benchmarking tests have been completed on a 5m and 2.5m model on a single GPU card.

{| align="center" class="wikitable"
! style="background-color:#005581; font-weight:bold; color:white;" | Processor Name
! style="background-color:#005581; font-weight:bold; color:white;" width=24% | Graphics Card**
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | GPU RAM (GB)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Number of CUDA Cores*
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Runtime 2.5m (mins)
! style="background-color:#005581; font-weight:bold; color:white;" width=8% | Combined Runtime (mins)
! style="background-color:#C5C5C5; font-weight:bold; color:white;" width=12% | System Name
|-
|Intel(R) Xeon(R) CPU @ 2.30GHz ||NVIDIA Tesla V100||16||5120||155.2||1172.9||1328.1||FM-NODE: Tesla V100
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||203.8||1523.9||1727.7||ACH
|-
|AMD Ryzen Threadripper 2950X 16-Core Processor ||NVIDIA TITAN RTX||24||4608||201.2||1548.1||1749.3||JGR
|-
|Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||220.0||1634.5||1854.5||MA1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 Ti||11||4352||222.2||1648.7||1870.9||JPI
|-
|Intel(R) Core(TM) i9-9900K CPU @ (5.10GHz) ||NVIDIA GeForce RTX 2080 (core 2100MHz, mem 8000MHz)||8||2944||241.2||1863.5||2104.7||RRB
|-
|Intel(R) Core(TM) i9-9900KF CPU @ 3.60GHz ||NVIDIA GeForce RTX 2080 SUPER||8||3072||257.3||1957.7||2215.0||RH2
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce RTX 2080||8||2944||275.1||2147.4||2422.5||PM2
|-
|AMD Ryzen Threadripper 2990WX 32-Core Processor ||NVIDIA TITAN Xp||12||3840||296.0||2218.4||2514.4||FLC
|-
|Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||298.9||2290.1||2589.0||JS1
|-
|Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.3||2345.1||2656.4||615
|-
|Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||310.3||2377.2||2687.5||RCD
|-
|Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||308.7||2384.7||2693.4||RH1
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||311.9||2404.9||2716.7||PM1
|-
|Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz ||NVIDIA GeForce GTX 1080 Ti||11||3584||324.8||2475.3||2800.1||HNM
|-
|Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz ||NVIDIA GeForce GTX 1080||8||2560||439.0||3379.3||3818.2||BLK
|-
|Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz ||NVIDIA GeForce GTX 1070||8||1920||475.5||3788.2||4263.7||SKI
|-
|}
<pre> * it is noted that the number of CUDA cores is not provided as an output from the '''dxdiag''' command and this information has been sourced from the nvidia website.
** The output cpu.txt only provides the 'out of the box' processor speed. If you have overclocked your cpu and/or gpu, please send these details to TUFLOW Support so we can add the overclocked data in brackets. </pre>

{{Tips Navigation
|uplink=[[Hardware_Benchmarking_(2018-03-AA) | Back to TUFLOW Benchmarking]]
}}

File:Viewer toolbar.jpeg

2020-08-25T13:22:40Z

TuflowJoe:

File:DV RES M01.jpeg

2020-08-25T12:38:50Z

TuflowJoe:

File:Depth res M01.jpeg

2020-08-25T12:36:32Z

TuflowJoe:

File:Load res.jpeg

2020-08-25T11:25:47Z

TuflowJoe:

File:Plugin directory.jpeg

2020-08-25T11:12:50Z

TuflowJoe:

Green-Ampt Infiltration Parameters

2020-07-10T14:41:08Z

TuflowJoe:

== Introduction ==
There are a number of methods available within TUFLOW to infiltrate water on the 2D surface into the sub-surface. These are Green-Ampt, Horton and Initial Loss/Continuing Loss. The models are used to represent hydrological losses particularly when direct rainfall is applied directly to the 2D surface and runoff is generated. As such, the infiltration module used, and the parameters selected, are important calibration parameters which should be used to calibrate simulated flows to observed flows. This is particularly important for whole of catchment modelling. 

== Green-Ampt Infiltration ==
The Green-Ampt approach varies the rate of infiltration over time based on the soil’s hydraulic conductivity, suction, porosity and initial moisture content. The method assumes that as water begins to infiltrate the soil, a line is developed differentiating between the ‘dry’ soil with moisture content θi) and the ‘wet’ soil (with moisture content equal to the porosity of the soil η). As the infiltrated water continues to move through the soil profile in a vertical direction, the soil moisture changes instantly from the initial content to a saturated state. This concept is shown in Figure 1. 
 

[[File:Fig_1_GA_Model.png|300px|Figure 1 Green-Ampt Model Concept]] 

'''Figure 1 Green-Ampt Model Concept''' 
''Figure courtesy of University of Texas''

 
The basic form of the Green-Ampt equation is expressed as follows: 
[[File:Basic_ga_equation.png|200px]] 

 
Where: 
:''t'' is time 
:K is the saturated hydraulic conductivity 
:∆''θ'' is defined as the soil capacity (the difference between the saturated and initial moisture content) 
:''φ'' is the soil suction head 
:h0 is the depth of ponded water 
:F(t) is the cumulative infiltration calculated from: 
[[File:Accumulative_infil.png|350px]] 

 
United States Department of Agriculture (USDA) soil types have been hardwired into TUFLOW and are presented in Table 1 along with the soil parameters. Alternatively, it is possible to define a customised soil type by specifying user defined values within the tsoilf. 
 
'''Table 1 USDA Soil types for the Green-Ampt Infiltration Method (from Rawls, W, J, Brakesiek & Miller, N, 1983, ‘Green-Ampt infiltration parameters from soils data’, Journal of Hydraulic Engineering, vol 109, 62-71.)''' 
{| class="wikitable "
| '''USDA Soil Type''' || '''Suction (mm)''' || '''Hydraulic Conductivity (mm/hr)''' || '''Porosity (Fraction)'''
|-
| '''Clay''' || 316.3 || 0.3 || 0.385
|-
| '''Silty Clay''' || 292.2 || 0.5 || 0.423
|-
| '''Sandy Clay''' || 239 || 0.6 || 0.321
|-
| '''Clay Loam''' || 208.8 || 1 || 0.309
|-
| '''Silty Clay Loam''' || 273 || 1 || 0.432
|-
| '''Sandy Clay Loam''' || 218.5 || 1.5 || 0.33
|-
| '''Silt Loam''' || 166.8 || 3.4 || 0.486
|-
| '''Loam''' || 88.9 || 7.6 || 0.434
|-
| '''Sandy Loam''' || 110.1 || 10.9 || 0.412
|-
| '''Loamy Sand''' || 61.3 || 29.9 || 0.401
|-
| '''Sand''' || 49.5 || 117.8 || 0.417
|}
 
Table 2 presents summary statistics for the Green-Ampt USDA Parameters and typical values. This provides a good indication of the typical ranges of the Green-Ampt parameter values. 
 
'''Table 2 USDA Summary Statistics for all Soil types for the Green-Ampt Infiltration Method''' 
{| class="wikitable "
| '''Stat''' || '''Suction (mm)''' || '''Hydraulic Conductivity (mm/hr)''' || '''Porosity (Fraction)'''
|-
| '''Min''' || 49.5 || 0.3 || 0.31
|-
| '''Max''' || 316.3 || 117.8 || 0.49
|-
| '''Mean''' || 184.04 || 15.86 || 0.4
|-
| '''SD''' || 94.82 || 34.92 || 0.05
|}
 
In order to help calibration off TUFLOW models to observed data, a sensitivity analysis of the various parameters have been undertaken to show the effect of each parameter in isolation. The comparison has been undertaken on a real-world whole catchment model of the Plynlimon catchment in mid-Wales. The model was run with a real rainfall event from 2015 with a temporal resolution of 30 minutes as shown in Figure 2. 
 

[[File:Fig2 GWY RF.png|600px|Figure 2: Plynlimon Rainfall]] 

'''Figure 2: Plynlimon Rainfall''' 

For the purposes of this sensitivity analysis of the parameters a single soil type was used representing the general clay soil types that are present. 

== Green-Ampt Infiltration: User Parameters ==
Where the inbuilt USDA soil types are not used, the user can specify their own values for the Suction, Hydraulic Conductivity, Porosity and Initial Soil Moisture. What follows is a description of each parameter and the sensitivity to a low, medium and high value based on the USDA soil type summary values. 

=== Capillary Suction Head ===
The suction head, represented in millimeters, is the capillary attraction on the soil voids. It is large for fine grain soils such as clays and smaller for sandy soils. 
To test the sensitivity of the simulated runoff at a gauged location, a low (49.5mm), mid representing the mean (184.4mm) and high (316.3mm) value of the suction head parameter were used with other parameters representing a clay soil (soil type 1). 
The larger the value the capillary suction head value, the more capillary action that is achieved and the amount of infiltration that takes place. This is shown by the increase in cumulative infiltration in the graph below with a greater cumulative infiltration for the increase in the suction head. 

[[File:Fig4 sens to suction.png|600px|Figure 3: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Capillary Suction Head parameter in the Green-Ampt infiltration model.]] 

'''Figure 3: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Capillary Suction Head parameter in the Green-Ampt infiltration model.''' 
 
As a consequence of this, there is a less runoff generated as shown in Figure 4. As can be seen, the model is not particularly sensitive to the suction head parameter and this fits with observations made within the literature from other similar studies. 
 

[[File:Fig1.jpeg|600px|Figure 4: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the Suction head parameter in the Green-Ampt infiltration model.]] 

'''Figure 4: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the Suction head parameter in the Green-Ampt infiltration model.''' 
 
It can also be seen that the higher the suction head value that the longer it takes the hydrograph to start rising, with the high suction head scenario less responsive to the rainfall.
 

=== Saturated Hydraulic Conductivity ===
The saturated hydraulic conductivity, measured in mm per hour, represents the ease that water can travel through the soil whilst it is saturated. The saturated hydraulic conductivity is the equivalent of the limiting infiltration rate in the Horton infiltration model. The hydraulic conductivity is high for sandy soils but low for compact clays. Again, the sensitivity was conducted by varying the value for clays, which itself is relatively by low, with three scenarios, low (0.3mm/hr), mid representing the mean (15.86mm/hr) and high (117.8mm/hr). The results shown in figure 5, show that the parameter is very sensitive to the changes in the hydraulic conductivity with the mid and high values providing significant infiltration such that no runoff is generated and the flow is zero at the downstream gauge. The importance of hydraulic conductivity in the calculation of infiltration with the Green-Ampt equation has been well documented. As expected, the higher the hydraulic conductivity, then the more infiltration that occurs and the less runoff that is generated. 
 

[[File:Fig6 hydroconduct.png|600px|Figure 5: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model when using a clay soil type.]] 

'''Figure 5: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model when using a clay soil type.''' 
 

[[File:Fig7 hydroconduct.png|600px|Figure 6: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model.]] 

'''Figure 6: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model.''' 

=== Porosity ===
The porosity value represents the volume of dry voids per volume of soil and provides the maximum moisture deficit that is available, the difference between the moisture content at saturation and at the start of the simulation. 
Sandy soils tend to have lower porosities than clay soils, but drain to lower moisture contents between rainfall events because water is not held as strongly in the soil pores. Therefore, values of porosity tend to be higher for sandy soils when compared to clay soils. 
As shown in figure 7, the higher the porosity value, then the less runoff that is generated due to increased infiltration although the model is not particular sensitive to the porosity value. 
 

[[File:Fig7 porosity sens.png|600px|Figure 7: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.]] 

'''Figure 7: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.''' 
 

[[File:Fig8 porosity sens.png|600px|Figure 8: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.]] 

'''Figure 8: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.''' 
 

=== Initial Moisture ===
The initial moisture value represents the fraction of the soil that is initially wet. As both initial moisture and porosity are expressed as fractions, the soil capacity is defined as the difference between them both. As such, the initial moisture should not exceed the porosity otherwise soil capacity will be set to zero with no infiltration occurring for that soil type. A [[TUFLOW Message 2508 | WARNING]] of 2508 is issued if this is the case. 

As you increase initial moisture at the beginning of your simulation, you experience less infiltration (as you are closer to the soil capacity), therefore have more run-off and a quicker response. Figure 9 shows the degree of change to cumulative infiltration with varying initial moisture and the effect on the catchment can be seen in Figure 10.

[[File:Init moisture F10.png|600px|Figure 9: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the initial moisture parameter in the Green-Ampt infiltration model.]] 
'''Figure 9: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the initial moisture parameter in the Green-Ampt infiltration model.''' 
[[File:Init moisture F11 catchment.png|600px|Figure 10: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the initial moisture parameter in the Green-Ampt infiltration model.]] 
'''Figure 10: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the initial moisture parameter in the Green-Ampt infiltration model.''' 

== In built USDA soil type ==
The model was also run with the default in-build USDA soil types. Figure 11 shows the outputs. As expected the higher the soil type, then typically the more the infiltration and the lower the produced runoff. Soils 8-11, which represent sandy soils do not show any runoff in this example as the rainfall applied directly to the mesh is all infiltrated. 
 
[[File:Fig10 GA soils.png|600px|Figure 11: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the USDA soil type parameter in the Green-Ampt infiltration model.]] 
'''Figure 11: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the USDA soil type parameter in the Green-Ampt infiltration model.'''
 

== Summary ==
The Green-Ampt infiltration model is one of the available infiltration models within TUFLOW. There is a wide range of literature on Green-Ampt applications and some suggested parameter values for particular soil types, albeit mostly soil types from the US. The 3 main Green-Ampt parameters have been tested to show the sensitivity of model outputs to the values as well as the variation in initial moisture. The results show that the model is relatively insensitive to the porosity value and suction head parameter. However, the outputs do show significant variations in runoff volume due to variations in both hydraulic conductivity. As part of any calibration exercise it is suggested that it is the hydraulic conductivity, in conjunction with the initial moisture content, which would be the parameters that are most focused on. The hydraulic conductivity appears to affect the runoff volume throughout the event whereas the initial soil moisture has a limited impact at the beginning of the event before soils become saturated and results converge.

Green-Ampt Infiltration Parameters

2020-07-10T14:26:20Z

TuflowJoe:

== Introduction ==
There are a number of methods available within TUFLOW to infiltrate water on the 2D surface into the sub-surface. These are Green-Ampt, Horton and Initial Loss/Continuing Loss. The models are used to represent hydrological losses particularly when direct rainfall is applied directly to the 2D surface and runoff is generated. As such, the infiltration module used, and the parameters selected, are important calibration parameters which should be used to calibrate simulated flows to observed flows. This is particularly important for whole of catchment modelling. 

== Green-Ampt Infiltration ==
The Green-Ampt approach varies the rate of infiltration over time based on the soil’s hydraulic conductivity, suction, porosity and initial moisture content. The method assumes that as water begins to infiltrate the soil, a line is developed differentiating between the ‘dry’ soil with moisture content θi) and the ‘wet’ soil (with moisture content equal to the porosity of the soil η). As the infiltrated water continues to move through the soil profile in a vertical direction, the soil moisture changes instantly from the initial content to a saturated state. This concept is shown in Figure 1. 
 

[[File:Fig_1_GA_Model.png|300px|Figure 1 Green-Ampt Model Concept]] 

'''Figure 1 Green-Ampt Model Concept''' 
''Figure courtesy of University of Texas''

 
The basic form of the Green-Ampt equation is expressed as follows: 
[[File:Basic_ga_equation.png|200px]] 

 
Where: 
:''t'' is time 
:K is the saturated hydraulic conductivity 
:∆''θ'' is defined as the soil capacity (the difference between the saturated and initial moisture content) 
:''φ'' is the soil suction head 
:h0 is the depth of ponded water 
:F(t) is the cumulative infiltration calculated from: 
[[File:Accumulative_infil.png|350px]] 

 
United States Department of Agriculture (USDA) soil types have been hardwired into TUFLOW and are presented in Table 1 along with the soil parameters. Alternatively, it is possible to define a customised soil type by specifying user defined values within the tsoilf. 
 
'''Table 1 USDA Soil types for the Green-Ampt Infiltration Method (from Rawls, W, J, Brakesiek & Miller, N, 1983, ‘Green-Ampt infiltration parameters from soils data’, Journal of Hydraulic Engineering, vol 109, 62-71.)''' 
{| class="wikitable "
| '''USDA Soil Type''' || '''Suction (mm)''' || '''Hydraulic Conductivity (mm/hr)''' || '''Porosity (Fraction)'''
|-
| '''Clay''' || 316.3 || 0.3 || 0.385
|-
| '''Silty Clay''' || 292.2 || 0.5 || 0.423
|-
| '''Sandy Clay''' || 239 || 0.6 || 0.321
|-
| '''Clay Loam''' || 208.8 || 1 || 0.309
|-
| '''Silty Clay Loam''' || 273 || 1 || 0.432
|-
| '''Sandy Clay Loam''' || 218.5 || 1.5 || 0.33
|-
| '''Silt Loam''' || 166.8 || 3.4 || 0.486
|-
| '''Loam''' || 88.9 || 7.6 || 0.434
|-
| '''Sandy Loam''' || 110.1 || 10.9 || 0.412
|-
| '''Loamy Sand''' || 61.3 || 29.9 || 0.401
|-
| '''Sand''' || 49.5 || 117.8 || 0.417
|}
 
Table 2 presents summary statistics for the Green-Ampt USDA Parameters and typical values. This provides a good indication of the typical ranges of the Green-Ampt parameter values. 
 
'''Table 2 USDA Summary Statistics for all Soil types for the Green-Ampt Infiltration Method''' 
{| class="wikitable "
| '''Stat''' || '''Suction (mm)''' || '''Hydraulic Conductivity (mm/hr)''' || '''Porosity (Fraction)'''
|-
| '''Min''' || 49.5 || 0.3 || 0.31
|-
| '''Max''' || 316.3 || 117.8 || 0.49
|-
| '''Mean''' || 184.04 || 15.86 || 0.4
|-
| '''SD''' || 94.82 || 34.92 || 0.05
|}
 
In order to help calibration off TUFLOW models to observed data, a sensitivity analysis of the various parameters have been undertaken to show the effect of each parameter in isolation. The comparison has been undertaken on a real-world whole catchment model of the Plynlimon catchment in mid-Wales. The model was run with a real rainfall event from 2015 with a temporal resolution of 30 minutes as shown in Figure 2. 
 

[[File:Fig2 GWY RF.png|600px|Figure 2: Plynlimon Rainfall]] 

'''Figure 2: Plynlimon Rainfall''' 

For the purposes of this sensitivity analysis of the parameters a single soil type was used representing the general clay soil types that are present. 

== Green-Ampt Infiltration: User Parameters ==
Where the inbuilt USDA soil types are not used, the user can specify their own values for the Suction, Hydraulic Conductivity, Porosity and Initial Soil Moisture. What follows is a description of each parameter and the sensitivity to a low, medium and high value based on the USDA soil type summary values. 

=== Capillary Suction Head ===
The suction head, represented in millimeters, is the capillary attraction on the soil voids. It is large for fine grain soils such as clays and smaller for sandy soils. 
To test the sensitivity of the simulated runoff at a gauged location, a low (49.5mm), mid representing the mean (184.4mm) and high (316.3mm) value of the suction head parameter were used with other parameters representing a clay soil (soil type 1). 
The larger the value the capillary suction head value, the more capillary action that is achieved and the amount of infiltration that takes place. This is shown by the increase in cumulative infiltration in the graph below with a greater cumulative infiltration for the increase in the suction head. 

[[File:Fig4 sens to suction.png|600px|Figure 3: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Capillary Suction Head parameter in the Green-Ampt infiltration model.]] 

'''Figure 3: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Capillary Suction Head parameter in the Green-Ampt infiltration model.''' 
 
As a consequence of this, there is a less runoff generated as shown in Figure 4. As can be seen, the model is not particularly sensitive to the suction head parameter and this fits with observations made within the literature from other similar studies. 
 

[[File:Fig1.jpeg|600px|Figure 4: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the Suction head parameter in the Green-Ampt infiltration model.]] 

'''Figure 4: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the Suction head parameter in the Green-Ampt infiltration model.''' 
 
It can also be seen that the higher the suction head value that the longer it takes the hydrograph to start rising, with the high suction head scenario less responsive to the rainfall.
 

=== Saturated Hydraulic Conductivity ===
The saturated hydraulic conductivity, measured in mm per hour, represents the ease that water can travel through the soil whilst it is saturated. The saturated hydraulic conductivity is the equivalent of the limiting infiltration rate in the Horton infiltration model. The hydraulic conductivity is high for sandy soils but low for compact clays. Again, the sensitivity was conducted by varying the value for clays, which itself is relatively by low, with three scenarios, low (0.3mm/hr), mid representing the mean (15.86mm/hr) and high (117.8mm/hr). The results shown in figure 5, show that the parameter is very sensitive to the changes in the hydraulic conductivity with the mid and high values providing significant infiltration such that no runoff is generated and the flow is zero at the downstream gauge. The importance of hydraulic conductivity in the calculation of infiltration with the Green-Ampt equation has been well documented. As expected, the higher the hydraulic conductivity, then the more infiltration that occurs and the less runoff that is generated. 
 

[[File:Fig6 hydroconduct.png|600px|Figure 5: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model when using a clay soil type.]] 

'''Figure 5: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model when using a clay soil type.''' 
 

[[File:Fig7 hydroconduct.png|600px|Figure 6: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model.]] 

'''Figure 6: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model.''' 

=== Porosity ===
The porosity value represents the volume of dry voids per volume of soil and provides the maximum moisture deficit that is available, the difference between the moisture content at saturation and at the start of the simulation. 
Sandy soils tend to have lower porosities than clay soils, but drain to lower moisture contents between rainfall events because water is not held as strongly in the soil pores. Therefore, values of porosity tend to be higher for sandy soils when compared to clay soils. 
As shown in figure 7, the higher the porosity value, then the less runoff that is generated due to increased infiltration although the model is not particular sensitive to the porosity value. 
 

[[File:Fig7 porosity sens.png|600px|Figure 7: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.]] 

'''Figure 7: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.''' 
 

[[File:Fig8 porosity sens.png|600px|Figure 8: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.]] 

'''Figure 8: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.''' 
 

=== Initial Moisture ===
The initial moisture value represents the fraction of the soil that is initially wet. As both initial moisture and porosity are expressed as fractions, the soil capacity is defined as the difference between them both. As such, the initial moisture should not exceed the porosity otherwise soil capacity will be set to zero with no infiltration occurring for that soil type. A [[TUFLOW Message 2508 | WARNING]] of 2508 is issued if this is the case. 

As you increase initial moisture at the beginning of your simulation, you experience less infiltration (as you are closer to the soil capacity), therefore have more run-off and a quicker response. Figure 9 shows the degree of change to cumulative infiltration with varying initial moisture and the effect on the catchment can be seen in Figure 10.

[[File:Init moisture F10.png|600px|Figure 9: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the initial moisture parameter in the Green-Ampt infiltration model.]] 
'''Figure 9: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the initial moisture parameter in the Green-Ampt infiltration model.''' 
[[File:Init moisture F11 catchment.png|600px|Figure 10: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the initial moisture parameter in the Green-Ampt infiltration model.]] 
'''Figure 10: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the initial moisture parameter in the Green-Ampt infiltration model.''' 

== In built USDA soil type ==
The model was also run with the default in-build USDA soil types. Figure 11 shows the outputs. As expected the higher the soil type, then typically the more the infiltration and the lower the produced runoff. Soils 8-11, which represent sandy soils do not show any runoff in this example as the rainfall applied directly to the mesh is all infiltrated. 
 
[[File:Fig10 GA soils.png|600px|Figure 11: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the USDA soil type parameter in the Green-Ampt infiltration model.]] 
'''Figure 9: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the USDA soil type parameter in the Green-Ampt infiltration model.'''
 

== Summary ==
The Green-Ampt infiltration model is one of the available infiltration models within TUFLOW. There is a wide range of literature on Green-Ampt applications and some suggested parameter values for particular soil types, albeit mostly soil types from the US. The 3 main Green-Ampt parameters have been tested to show the sensitivity of model outputs to the values as well as the variation in initial moisture. The results show that the model is relatively insensitive to the porosity value and suction head parameter. However, the outputs do show significant variations in runoff volume due to variations in both hydraulic conductivity. As part of any calibration exercise it is suggested that it is the hydraulic conductivity, in conjunction with the initial moisture content, which would be the parameters that are most focused on. The hydraulic conductivity appears to affect the runoff volume throughout the event whereas the initial soil moisture has a limited impact at the beginning of the event before soils become saturated and results converge.

Green-Ampt Infiltration Parameters

2020-07-10T14:21:45Z

TuflowJoe:

== Introduction ==
There are a number of methods available within TUFLOW to infiltrate water on the 2D surface into the sub-surface. These are Green-Ampt, Horton and Initial Loss/Continuing Loss. The models are used to represent hydrological losses particularly when direct rainfall is applied directly to the 2D surface and runoff is generated. As such, the infiltration module used, and the parameters selected, are important calibration parameters which should be used to calibrate simulated flows to observed flows. This is particularly important for whole of catchment modelling. 

== Green-Ampt Infiltration ==
The Green-Ampt approach varies the rate of infiltration over time based on the soil’s hydraulic conductivity, suction, porosity and initial moisture content. The method assumes that as water begins to infiltrate the soil, a line is developed differentiating between the ‘dry’ soil with moisture content θi) and the ‘wet’ soil (with moisture content equal to the porosity of the soil η). As the infiltrated water continues to move through the soil profile in a vertical direction, the soil moisture changes instantly from the initial content to a saturated state. This concept is shown in Figure 1. 
 

[[File:Fig_1_GA_Model.png|300px|Figure 1 Green-Ampt Model Concept]] 

'''Figure 1 Green-Ampt Model Concept''' 
''Figure courtesy of University of Texas''

 
The basic form of the Green-Ampt equation is expressed as follows: 
[[File:Basic_ga_equation.png|200px]] 

 
Where: 
:''t'' is time 
:K is the saturated hydraulic conductivity 
:∆''θ'' is defined as the soil capacity (the difference between the saturated and initial moisture content) 
:''φ'' is the soil suction head 
:h0 is the depth of ponded water 
:F(t) is the cumulative infiltration calculated from: 
[[File:Accumulative_infil.png|350px]] 

 
United States Department of Agriculture (USDA) soil types have been hardwired into TUFLOW and are presented in Table 1 along with the soil parameters. Alternatively, it is possible to define a customised soil type by specifying user defined values within the tsoilf. 
 
'''Table 1 USDA Soil types for the Green-Ampt Infiltration Method (from Rawls, W, J, Brakesiek & Miller, N, 1983, ‘Green-Ampt infiltration parameters from soils data’, Journal of Hydraulic Engineering, vol 109, 62-71.)''' 
{| class="wikitable "
| '''USDA Soil Type''' || '''Suction (mm)''' || '''Hydraulic Conductivity (mm/hr)''' || '''Porosity (Fraction)'''
|-
| '''Clay''' || 316.3 || 0.3 || 0.385
|-
| '''Silty Clay''' || 292.2 || 0.5 || 0.423
|-
| '''Sandy Clay''' || 239 || 0.6 || 0.321
|-
| '''Clay Loam''' || 208.8 || 1 || 0.309
|-
| '''Silty Clay Loam''' || 273 || 1 || 0.432
|-
| '''Sandy Clay Loam''' || 218.5 || 1.5 || 0.33
|-
| '''Silt Loam''' || 166.8 || 3.4 || 0.486
|-
| '''Loam''' || 88.9 || 7.6 || 0.434
|-
| '''Sandy Loam''' || 110.1 || 10.9 || 0.412
|-
| '''Loamy Sand''' || 61.3 || 29.9 || 0.401
|-
| '''Sand''' || 49.5 || 117.8 || 0.417
|}
 
Table 2 presents summary statistics for the Green-Ampt USDA Parameters and typical values. This provides a good indication of the typical ranges of the Green-Ampt parameter values. 
 
'''Table 2 USDA Summary Statistics for all Soil types for the Green-Ampt Infiltration Method''' 
{| class="wikitable "
| '''Stat''' || '''Suction (mm)''' || '''Hydraulic Conductivity (mm/hr)''' || '''Porosity (Fraction)'''
|-
| '''Min''' || 49.5 || 0.3 || 0.31
|-
| '''Max''' || 316.3 || 117.8 || 0.49
|-
| '''Mean''' || 184.04 || 15.86 || 0.4
|-
| '''SD''' || 94.82 || 34.92 || 0.05
|}
 
In order to help calibration off TUFLOW models to observed data, a sensitivity analysis of the various parameters have been undertaken to show the effect of each parameter in isolation. The comparison has been undertaken on a real-world whole catchment model of the Plynlimon catchment in mid-Wales. The model was run with a real rainfall event from 2015 with a temporal resolution of 30 minutes as shown in Figure 2. 
 

[[File:Fig2 GWY RF.png|600px|Figure 2: Plynlimon Rainfall]] 

'''Figure 2: Plynlimon Rainfall''' 

For the purposes of this sensitivity analysis of the parameters a single soil type was used representing the general clay soil types that are present. 

== Green-Ampt Infiltration: User Parameters ==
Where the inbuilt USDA soil types are not used, the user can specify their own values for the Suction, Hydraulic Conductivity, Porosity and Initial Soil Moisture. What follows is a description of each parameter and the sensitivity to a low, medium and high value based on the USDA soil type summary values. 

=== Capillary Suction Head ===
The suction head, represented in millimeters, is the capillary attraction on the soil voids. It is large for fine grain soils such as clays and smaller for sandy soils. 
To test the sensitivity of the simulated runoff at a gauged location, a low (49.5mm), mid representing the mean (184.4mm) and high (316.3mm) value of the suction head parameter were used with other parameters representing a clay soil (soil type 1). 
The larger the value the capillary suction head value, the more capillary action that is achieved and the amount of infiltration that takes place. This is shown by the increase in cumulative infiltration in the graph below with a greater cumulative infiltration for the increase in the suction head. 

[[File:Fig4 sens to suction.png|600px|Figure 3: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Capillary Suction Head parameter in the Green-Ampt infiltration model.]] 

'''Figure 3: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Capillary Suction Head parameter in the Green-Ampt infiltration model.''' 
 
As a consequence of this, there is a less runoff generated as shown in Figure 4. As can be seen, the model is not particularly sensitive to the suction head parameter and this fits with observations made within the literature from other similar studies. 
 

[[File:Fig1.jpeg|600px|Figure 4: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the Suction head parameter in the Green-Ampt infiltration model.]] 

'''Figure 4: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the Suction head parameter in the Green-Ampt infiltration model.''' 
 
It can also be seen that the higher the suction head value that the longer it takes the hydrograph to start rising, with the high suction head scenario less responsive to the rainfall.
 

=== Saturated Hydraulic Conductivity ===
The saturated hydraulic conductivity, measured in mm per hour, represents the ease that water can travel through the soil whilst it is saturated. The saturated hydraulic conductivity is the equivalent of the limiting infiltration rate in the Horton infiltration model. The hydraulic conductivity is high for sandy soils but low for compact clays. Again, the sensitivity was conducted by varying the value for clays, which itself is relatively by low, with three scenarios, low (0.3mm/hr), mid representing the mean (15.86mm/hr) and high (117.8mm/hr). The results shown in figure 5, show that the parameter is very sensitive to the changes in the hydraulic conductivity with the mid and high values providing significant infiltration such that no runoff is generated and the flow is zero at the downstream gauge. The importance of hydraulic conductivity in the calculation of infiltration with the Green-Ampt equation has been well documented. As expected, the higher the hydraulic conductivity, then the more infiltration that occurs and the less runoff that is generated. 
 

[[File:Fig6 hydroconduct.png|600px|Figure 5: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model when using a clay soil type.]] 

'''Figure 5: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model when using a clay soil type.''' 
 

[[File:Fig7 hydroconduct.png|600px|Figure 6: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model.]] 

'''Figure 6: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model.''' 

=== Porosity ===
The porosity value represents the volume of dry voids per volume of soil and provides the maximum moisture deficit that is available, the difference between the moisture content at saturation and at the start of the simulation. 
Sandy soils tend to have lower porosities than clay soils, but drain to lower moisture contents between rainfall events because water is not held as strongly in the soil pores. Therefore, values of porosity tend to be higher for sandy soils when compared to clay soils. 
As shown in figure 7, the higher the porosity value, then the less runoff that is generated due to increased infiltration although the model is not particular sensitive to the porosity value. 
 

[[File:Fig7 porosity sens.png|600px|Figure 7: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.]] 

'''Figure 7: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.''' 
 

[[File:Fig8 porosity sens.png|600px|Figure 8: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.]] 

'''Figure 8: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.''' 
 

=== Initial Moisture ===
The initial moisture value represents the fraction of the soil that is initially wet. As both initial moisture and porosity are expressed as fractions, the soil capacity is defined as the difference between them both. As such, the initial moisture should not exceed the porosity otherwise soil capacity will be set to zero with no infiltration occurring for that soil type. A WARNING of 2508 is issued if this is the case. 

As you increase initial moisture at the beginning of your simulation, you experience less infiltration (as you are closer to the soil capacity), therefore have more run-off and a quicker response. Figure 9 shows the degree of change to cumulative infiltration with varying initial moisture and the effect on the catchment can be seen in Figure 10.

[[File:Init moisture F10.png|600px|Figure 9: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the initial moisture parameter in the Green-Ampt infiltration model.]] 
'''Figure 9: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the initial moisture parameter in the Green-Ampt infiltration model.''' 
[[File:Init moisture F11 catchment.png|600px|Figure 10: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the initial moisture parameter in the Green-Ampt infiltration model.]] 
'''Figure 10: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the initial moisture parameter in the Green-Ampt infiltration model.''' 

== In built USDA soil type ==
The model was also run with the default in-build USDA soil types. Figure 11 shows the outputs. As expected the higher the soil type, then typically the more the infiltration and the lower the produced runoff. Soils 8-11, which represent sandy soils do not show any runoff in this example as the rainfall applied directly to the mesh is all infiltrated. 
 
[[File:Fig10 GA soils.png|600px|Figure 11: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the USDA soil type parameter in the Green-Ampt infiltration model.]] 
'''Figure 9: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the USDA soil type parameter in the Green-Ampt infiltration model.'''
 

== Summary ==
The Green-Ampt infiltration model is one of the available infiltration models within TUFLOW. There is a wide range of literature on Green-Ampt applications and some suggested parameter values for particular soil types, albeit mostly soil types from the US. The 3 main Green-Ampt parameters have been tested to show the sensitivity of model outputs to the values as well as the variation in initial moisture. The results show that the model is relatively insensitive to the porosity value and suction head parameter. However, the outputs do show significant variations in runoff volume due to variations in both hydraulic conductivity. As part of any calibration exercise it is suggested that it is the hydraulic conductivity, in conjunction with the initial moisture content, which would be the parameters that are most focused on. The hydraulic conductivity appears to affect the runoff volume throughout the event whereas the initial soil moisture has a limited impact at the beginning of the event before soils become saturated and results converge.

File:Init moisture F11 catchment.png

2020-07-10T14:16:41Z

TuflowJoe:

File:Init moisture F10.png

2020-07-10T14:14:50Z

TuflowJoe:

1D-2D Flood Modeller-TUFLOW

2020-07-03T11:43:32Z

TuflowJoe:

<ol>
=THIS PAGE IS CURRENTLY UNDER CONSTRUCTION=

=Introduction=
TUFLOW models can be configured to dynamically link to Flood Modeller by utilising a water level boundary to the 2D cells along the 1D/2D interface. In the 2D boundary condition (2d_bc) GIS layer, we define the location at which this link occurs. The 2D water level applied at the 2D boundary cells is calculated in the 1D Flood Modeller component. The terminology used in TUFLOW is a '''HX''' type boundary on the 2D cells, with the '''H''' indicating that a '''H'''ead (water level) boundary is used and the '''X''' indicating the value is coming from an e'''X'''ternal model (in this case Flood Modeller).
 
Depending on the water level in the surrounding 2D cells, flow can either enter or leave the HX cells. The volume of water entering or leaving the 2D boundary is added or subtracted from the 1D Flood Modeller model to preserve volume. We must connect the HX lines to the 1D Flood Modeller model. This is done using CN type lines in the 2d_bc layer, where a CN line is connected to the HX line, the water level from the 1D Flood Modeller nodes is transferred to the HX line. In between 1D nodes, a linear interpolation of water level is applied. This is shown in the figure below.
 
[[File:1d 2d FM link 01.jpg|800px]]
 
Once the water level in Flood Modeller exceeds the elevation in the boundary cell water can enter or leave the model. Similar to a Flood Modeller lateral spill or lateral inflow, the discharge is distributed laterally along the length of the HX line. Note that it is the elevation of the HX boundary cell centres that determines when the spill starts to occur and not the cross section within Flood Modeller. If there is a levee or flood defence, it is important that we use breaklines in the model to ensure that the elevations of the 2D cells are consistent with the levee crest. The next four images show a section view of the 1D/2D link and how this may progress during a flood event. 
 
[[File:M04 1d2d 01.png|300px]]
[[File:M04 1d2d 02.png|300px]]
[[File:M04 1d2d 03.png|300px]]
[[File:M04 1d2d 04.png|300px]]

Often HX lines are located along the top of a levee (natural or artificial) or flood defence running along the river bank. When carving a 1D channel through a 2D domain, the HX line must be either on the top of the levee or on the inside of the levee (closest to the channel). If the HX line is located on the other side of the levee away from the channel, the effect of the levee on water flow is '''not''' modelled. In the image above, it can be seen that the boundary cell is along the levee and the interaction between the channel and the floodplain (1D and 2D) occurs at the correct elevation. 

=Building a Flood Modeller-TUFLOW 1D-2D HX Connection=
Flood Modeller and TUFLOW will be considered linked if a Flood Modeller node in a 1d_x1D layer is snapped to a TUFLOW CN line which in turn is snapped to a TUFLOW HX line in a 2d_bc file. ‘CN’ or connection lines read the water level from Flood Modeller and transfers this to the HX line.
 

In the figure below, the water level is calculated in Flood Modeller at the nodes FC01.16, FC01.15 and FC01.14. These water levels are linearly interpolated along the lengths of the HX line on each of the left and right banks of the watercourse. When the water level exceeds the ZC elevation of the boundary cell, water is able to flow out onto the TUFLOW 2D floodplain.
 

The digitised direction of the HX and CN lines is not important. The CN lines however should be digitised approximately perpendicular to the direction of flow. Two CN lines are digitised for each node and snapped to the HX boundary lines along the left and right banks at vertices. Both CN and HX lines are digitised within the same 2d_bc file, on a basic approach, you only need to input the '''type''' attribute (either '''CN''' or '''HX'''). It is also recommended to input an '''f''' attribute value of '''1''' for CN lines for clarity, however if left at zero TUFLOW will by default change this value to 1. There are a range of Flags that can be used within the 2d_bc file, also D values to change elevations of the link cells and also A values to change the storage across the cells. Further information on these can be found within the TUFLOW manual.
 
The HX boundary lines should be digitised along the top of each bank such that the width between the lines approximately correlates to the width of the 1D channel. This is important so as not to either over/under compensate flow area between the two solvers.
 

[[File:FMT 1D-2D Linking QGIS.JPG|600px]]

Note that where there are junctions within the Flood Modeller 1D model (i.e. at structures), both the nodes immediately upstream and downstream must be connected to TUFLOW. Refer to the below figure where the junctions are circled in blue and the upstream and downstream RIVER units are circled in yellow. The HX lines must be broken between the junctions as this is a requirement of a linked Flood Modeller – TUFLOW model. 
In the example below, the HX lines are broken between FC01.35 and FC01.34 as a culvert is located between these nodes.
 
[[File:FM_junction.jpg|900px]]
[[File:FMT 1D-2D Broken HX Line.JPG|900px]]
 
=Building a Flood Modeller-TUFLOW 1D-2D SX Connection=
Flood Modeller can also be dynamically linked to outflow directly to the 2D TUFLOW domain at the start or end of a flood modeller reach. This can relate to tributary channels, small draining channels, and also the main channel if exiting into a 2D estuarine environment for example.
 
The image below shows the representation within Flood Modeller and TUFLOW of a simple flume model. As you can see in Flood Modeller, the end open channel section '''FMT_003''' links directly to a spill unit, housing the same cross-section data, which then links to a dummy HT boundary unit. This dummy unit is left empty and works as a link between Flood Modeller and TUFLOW. Taking the Flood Modeller schematisation across to TUFLOW, as we saw in the section above the open channel section is linked via a 1D node and 2d_bc CN/HX connection. The SX link comes into play by having another 1D node (which for clarity it is recommend having in a separate file to the Flood Modeller Nodes) which has the same ID as the dummy HT boundary, remember that Flood Modeller is case sensitive so name exactly as is in Flood Modeller. As the connection between the open channel section '''FMT_003''' and the dummy boundary happens in Flood Modeller, no literal link is needed between these in the TUFLOW GIS files. All that is needed otherwise is a 2d_bc file with a CN/SX link snapped to the dummy boundary node, this again can be in a separate file to the CN/HX links for clarity or can be in the same file.
 
[[File:FMT LINK SX.png|900px]]

=Checking the Link=

As with all aspects of modelling, checking your files are being applied as intended is important. The check file to use in the case of 1D 2D linking is of course the 1d_2d_check. The image below you can see the channel being modelled, the Flood Modeller nodes connected via a CN/HX link discussed earlier. The 1d_2d_check file is shown by the blue grid cells. This shows the user the cells being picked up by the HX link lines, it also houses a wealth of information such as the primary Flood Modeller node it links to and if any flags where used in the HX lines for example. Below we are showing the elevation picked up at each link cell, this can be extremely useful in finding any abnormally low cells that have perhaps been incorrectly picked up and are subsequently leading to out of bank flow which in reality wouldn't be there.

[[File:1d2d FMT check.png|900px]]

=Summary=
Flood Modeller and TUFLOW models can be dynamically coupled to enable a bi-directional connection between the two software using either HX or SX links. The linkage take place when an Flood Modeller node is snapped to a HX or SX boundary, this can be via a CN connection link. The dynamic coupling allows full integration between Flood Modeller networks and full floodplain modelling in TUFLOW 2D to capture complex overland flows.

1D-2D Flood Modeller-TUFLOW

2020-07-01T16:12:51Z

TuflowJoe:

<ol>
=THIS PAGE IS CURRENTLY UNDER CONSTRUCTION=

=Introduction=
TUFLOW models can be configured to dynamically link to Flood Modeller models by utilising a water level boundary to the 2D cells along the 1D/2D interface. In the 2D boundary condition (2d_bc) GIS layer, we define the location at which this link occurs. The 2D water level applied at the 2D boundary cells is calculated in the 1D Flood Modeller component. The terminology used in TUFLOW is a '''HX''' type boundary on the 2D cells, with the '''H''' indicating that a '''H'''ead (water level) boundary is used and the '''X''' indicating the value is coming from an e'''X'''ternal model (in this case Flood Modeller).
 
Depending on the water level in the surrounding 2D cells, flow can either enter or leave the HX cells. The volume of water entering or leaving the 2D boundary is added or subtracted from the 1D Flood Modeller model to preserve volume. We must connect the HX lines to the 1D Flood Modeller model. This is done using CN type lines in the 2d_bc layer, where a CN line is connected to the HX line, the water level from the 1D Flood Modeller nodes is transferred to the HX line. In between 1D nodes, a linear interpolation of water level is applied. This is shown in the figure below.
 
[[File:1d 2d FM link 01.jpg|800px]]
 
Once the water level in Flood Modeller exceeds the elevation in the boundary cell water can enter or leave the model. Similar to a Flood Modeller lateral spill or lateral inflow, the discharge is distributed laterally along the length of the HX line. Note that it is the elevation of the HX boundary cell centres that determines when the spill starts to occur and not the cross section within Flood Modeller. If there is a levee or flood defence, it is important that we use breaklines in the model to ensure that the elevations of the 2D cells are consistent with the levee crest. The next four images show a section view of the 1D/2D link and how this may progress during a flood event. 
 
[[File:M04 1d2d 01.png|300px]]
[[File:M04 1d2d 02.png|300px]]
[[File:M04 1d2d 03.png|300px]]
[[File:M04 1d2d 04.png|300px]]

Often HX lines are located along the top of a levee (natural or artificial) or flood defence running along the river bank. When carving a 1D channel through a 2D domain, the HX line must be either on the top of the levee or on the inside of the levee (closest to the channel). If the HX line is located on the other side of the levee away from the channel, the effect of the levee on water flow is '''not''' modelled. In the image above, it can be seen that the boundary cell is along the levee and the interaction between the channel and the floodplain (1D and 2D) occurs at the correct elevation. 

=Building a Flood Modeller-TUFLOW 1D-2D HX Connection=
Flood Modeller and TUFLOW will be considered linked if a Flood Modeller node in a 1d_x1D layer is snapped to a TUFLOW CN line which in turn is snapped to a TUFLOW HX line in a 2d_bc file. ‘CN’ or connection lines read the water level from Flood Modeller and transfers this to the HX line.
 

In the figure below, the water level is calculated in Flood Modeller at the nodes FC01.16, FC01.15 and FC01.14. These water levels are linearly interpolated along the lengths of the HX line on each of the left and right banks of the watercourse. When the water level exceeds the ZC elevation of the boundary cell, water is able to flow out onto the TUFLOW 2D floodplain.
 

The digitised direction of the HX and CN lines is not important. The CN lines however should be digitised approximately perpendicular to the direction of flow. Two CN lines are digitised for each node and snapped to the HX boundary lines along the left and right banks at vertices. Both CN and HX lines are digitised within the same 2d_bc file, on a basic approach, you only need to input the '''type''' attribute (either '''CN''' or '''HX'''). It is also recommended to input an '''f''' attribute value of '''1''' for CN lines for clarity, however if left at zero TUFLOW will by default change this value to 1. There are a range of Flags that can be used within the 2d_bc file, also D values to change elevations of the link cells and also A values to change the storage across the cells. Further information on these can be found within the TUFLOW manual.
 
The HX boundary lines should be digitised along the top of each bank such that the width between the lines approximately correlates to the width of the 1D channel. This is important so as not to either over/under compensate flow area between the two solvers.
 

[[File:FMT 1D-2D Linking QGIS.JPG|600px]]

Note that where there are junctions within the Flood Modeller 1D model (i.e. at structures), both the nodes immediately upstream and downstream must be connected to TUFLOW. Refer to the below figure where the junctions are circled in blue and the upstream and downstream RIVER units are circled in yellow. The HX lines must be broken between the junctions as this is a requirement of a linked Flood Modeller – TUFLOW model. 
In the example below, the HX lines are broken between FC01.35 and FC01.34 as a culvert is located between these nodes.
 
[[File:FM_junction.jpg|900px]]
[[File:FMT 1D-2D Broken HX Line.JPG|900px]]
 
=Building a Flood Modeller-TUFLOW 1D-2D SX Connection=
Flood Modeller can also be dynamically linked to outflow directly to the 2D TUFLOW domain at the end of a flood modeller reach. This can relate to tributary channels, small draining channels, and also the main channel for example if exiting into a 2D estuarine environment for example.
 
The image below shows the representation within Flood Modeller and TUFLOW of a simple flume model. As you can see in Flood Modeller, the end open channel section '''FMT_003''' links directly to a spill unit, housing the same cross-section data, which then links to a dummy HT boundary unit. This dummy unit is left empty and works as a link between Flood Modeller and TUFLOW. Taking the Flood Modeller schematisation across to TUFLOW, as we saw in the section above the open channel section is linked via a 1D node and 2d_bc CN/HX connection. The SX link comes into play by having another 1D node (which for clarity it is recommend having in a separate file to the Flood Modeller Nodes) which has the same ID as the dummy HT boundary, remember that Flood Modeller is case sensitive so name exactly as is in Flood Modeller. As the connection between the open channel section '''FMT_003''' and the dummy boundary happens in Flood Modeller, no literal link is needed between these in the TUFLOW GIS files. All that is needed otherwise is a 2d_bc file with a CN/SX link snapped to the dummy boundary node, this again can be in a separate file to the CN/HX links for clarity or can be in the same file.
 
[[File:FMT LINK SX.png|900px]]

=Checking the Link=

As with all aspects of modelling, checking your files are being applied as intended is important. The check file to use in the case of 1D 2D linking is of course the 1d_2d_check. The image below you can see the channel being modelled, the Flood Modeller nodes connected via a CN/HX link discussed earlier. The 1d_2d_check file is shown by the blue grid cells. This shows the user the cells being picked up by the HX link lines, it also houses a wealth of information such as the primary Flood Modeller node it links to and if any flags where used in the HX lines for example. Below we are showing the elevation picked up at each link cell, this can be extremely useful in finding any abnormally low cells that have perhaps been incorrectly picked up and are subsequently leading to out of bank flow which in reality wouldn't be there.

[[File:1d2d FMT check.png|900px]]

=Summary=
Flood Modeller and TUFLOW models can be dynamically coupled to enable a bi-directional connection between the two software using either HX or SX links. The linkage take place when an Flood Modeller node is snapped to a HX or SX boundary, this can be via a CN connection link. The dynamic coupling allows full integration between Flood Modeller networks and full floodplain modelling in TUFLOW 2D to capture complex overland flows.

File:1d2d FMT check.png

2020-07-01T15:52:52Z

TuflowJoe:

1D-2D Flood Modeller-TUFLOW

2020-07-01T14:37:29Z

TuflowJoe:

File:FMT LINK SX.png

2020-07-01T14:35:19Z

TuflowJoe:

1D-2D Flood Modeller-TUFLOW

2020-07-01T14:32:10Z

TuflowJoe:

1D-2D Flood Modeller-TUFLOW

2020-07-01T14:04:38Z

TuflowJoe:

1D-2D Flood Modeller-TUFLOW

2020-07-01T13:39:34Z

TuflowJoe:

1D-2D Flood Modeller-TUFLOW

2020-07-01T13:13:30Z

TuflowJoe:

1D-2D Flood Modeller-TUFLOW

2020-07-01T12:28:41Z

TuflowJoe: Created page with "<ol> =THIS PAGE IS CURRENTLY UNDER CONSTRUCTION= =Introduction= In this section we will apply a water level boundary to the 2D cells along the 1D/2D interface. In the 2D boun..."

<ol>
=THIS PAGE IS CURRENTLY UNDER CONSTRUCTION=

=Introduction=
In this section we will apply a water level boundary to the 2D cells along the 1D/2D interface. In the 2D boundary condition (2d_bc) GIS layer, we define the location at which this link occurs. The 2D water level applied at the 2D boundary cells is calculated in the 1D Flood Modeller component. The terminology used in TUFLOW is a '''HX''' type boundary on the 2D cells, with the '''H''' indicating that a '''H'''ead (water level) boundary is used and the '''X''' indicating the value is coming from an e'''X'''ternal model (in this case Flood Modeller).
Depending on the water level in the surrounding 2D cells, flow can either enter or leave the HX cells. The volume of water entering or leaving the 2D boundary is added or subtracted from the 1D Flood Modeller model to preserve volume. We must connect the HX lines to the 1D Flood Modeller model. This is done using CN type lines in the 2d_bc layer, where a CN line is connected to the HX line, the water level from the 1D Flood Modeller nodes is transferred to the HX line. In between 1D nodes, a linear interpolation of water level is applied. This is shown in the figure below.
 
[[File:1d 2d FM link 01.jpg|800px]]
 
Once the water level in Flood Modeller exceeds the elevation in the boundary cell water can enter or leave the model. Similar to a Flood Modeller lateral spill or lateral inflow, the discharge is distributed laterally along the length of the HX line. Note that it is the elevation of the HX boundary cell centres that determines when the spill starts to occur and not the cross section within Flood Modeller. If there is a levee or flood defence, it is important that we use breaklines in the model to ensure that the elevations of the 2D cells are consistent with the levee crest. This will be described in the next section of the tutorial. The next four images show a section view of the 1D/2D link and how this may progress during a flood event. 
 
[[File:M04 1d2d 01.png|300px]]
[[File:M04 1d2d 02.png|300px]]
[[File:M04 1d2d 03.png|300px]]
[[File:M04 1d2d 04.png|300px]]

Often HX lines are located along the top of a levee (natural or artificial) or flood defence running along the river bank. When carving a 1D channel through a 2D domain, the HX line must be either on the top of the levee or on the inside of the levee (closest to the channel). If the HX line is located on the other side of the levee away from the channel, the effect of the levee on water flow is '''not''' modelled. In the sections above, it can be seen that the boundary cell is along the levee and the interaction between the channel and the floodplain (1D and 2D) occurs at the correct elevation. 

=Building an Flood Modeller-TUFLOW 1D-2D Connection=
Flood Modeller and TUFLOW will be considered linked if an Flood Modeller node in a 1d_x1D layer is snapped to a TUFLOW CN line which in turn is snapped to a TUFLOW HX line in a 2d_bc file. ‘CN’ or connection lines read the water level from Flood Modeller and transfers this to the HX line.
 

In the figure below, the water level is calculated in Flood Modeller at the nodes FC01.16, FC01.15 and FC01.14. These water levels are linearly interpolated along the lengths of the HX line on each of the left and right banks of the watercourse. When the water level exceeds the ZC elevation of the boundary cell, water is able to flow out onto the TUFLOW 2D floodplain.
 
[[File:FMT 1D-2D Linking QGIS.JPG|600px]]

Green-Ampt Infiltration Parameters

2020-06-01T15:29:48Z

TuflowJoe:

== '''PAGE UNDER CONSTRUCTION''' ==

== Introduction ==
There are a number of methods available within TUFLOW to infiltrate water on the 2D surface into the sub-surface. These are Green-Ampt, Horton and Initial Loss/Continuing Loss. The models are used to represent hydrological losses particularly when direct rainfall is applied directly to the 2D surface and runoff is generated. As such, the infiltration module used, and the parameters selected, are important calibration parameters which should be used to calibrate simulated flows to observed flows. 

== Green-Ampt Infiltration ==
The Green-Ampt approach varies the rate of infiltration over time based on the soil’s hydraulic conductivity, suction, porosity and initial moisture content. The method assumes that as water begins to infiltrate the soil, a line developed differentiating between the ‘dry’ soil with moisture content θ_i) and the ‘wet’ soil (with moisture content equal to the porosity of the soil η). As the infiltrated water continues to move through the soil profile in a vertical direction, the soil moisture changes instantly from the initial content to a saturated state. This concept is shown in Figure 1. 
 

[[File:Fig_1_GA_Model.png|300px|Figure 1 Green-Ampt Model Concept]] 

'''Figure 1 Green-Ampt Model Concept''' 
''Figure courtesy of University of Texas''

 
The basic form of the Green-Ampt equation is expressed as follows: 
[[File:Basic_ga_equation.png|200px]] 

 
Where: 
:''t'' is time 
:K is the saturated hydraulic conductivity 
:∆''θ'' is defined as the soil capacity (the difference between the saturated and initial moisture content) 
:''φ'' is the soil suction head 
:h0 is the depth of ponded water 
:F(t) is the cumulative infiltration calculated from: 
[[File:Accumulative_infil.png|350px]] 

 
United States Department of Agriculture (USDA) soil types have been hardwired into TUFLOW and are presented in Table 1 along with the soil parameters. Alternatively, it is possible to define a customised soil type by specifying user defined values within the tsoilf. 
 
'''Table 1 USDA Soil types for the Green-Ampt Infiltration Method''' 
{| class="wikitable "
| '''USDA Soil Type''' || '''Suction (mm)''' || '''Hydraulic Conductivity (mm/hr)''' || '''Porosity (Fraction)'''
|-
| '''Clay''' || 316.3 || 0.3 || 0.385
|-
| '''Silty Clay''' || 292.2 || 0.5 || 0.423
|-
| '''Sandy Clay''' || 239 || 0.6 || 0.321
|-
| '''Clay Loam''' || 208.8 || 1 || 0.309
|-
| '''Silty Clay Loam''' || 273 || 1 || 0.432
|-
| '''Sandy Clay Loam''' || 218.5 || 1.5 || 0.33
|-
| '''Silt Loam''' || 166.8 || 3.4 || 0.486
|-
| '''Loam''' || 88.9 || 7.6 || 0.434
|-
| '''Sandy Loam''' || 110.1 || 10.9 || 0.412
|-
| '''Loamy Sand''' || 61.3 || 29.9 || 0.401
|-
| '''Sand''' || 49.5 || 117.8 || 0.417
|}
 
Table 2 presents summary statistics for the Green-Ampt Parameters types. 
 
'''Table 2 USDA Summary Statistics for all Soil types for the Green-Ampt Infiltration Method''' 
{| class="wikitable "
| '''Stat''' || '''Suction (mm)''' || '''Hydraulic Conductivity (mm/hr)''' || '''Porosity (Fraction)'''
|-
| '''Min''' || 49.5 || 0.3 || 0.31
|-
| '''Max''' || 316.3 || 117.8 || 0.49
|-
| '''Mean''' || 184.04 || 15.86 || 0.4
|-
| '''SD''' || 94.82 || 34.92 || 0.05
|}
 
The comparison has been undertaken on a real-world model of the Plynlimon catchment in mid-Wales. The model was run with a real rainfall event from 2015 with a temporal resolution of 30 minutes as shown in the Figure 2. 
 

[[File:Fig2 GWY RF.png|600px|Figure 2: Plynlimon Rainfall]] 

'''Figure 2: Plynlimon Rainfall''' 
Within the Plynlimon catchment, the soil types were distributed within the catchment as shown in the map below although a single soil type has been selected for the comparison. Therefore, the effect seen is a consequence of changing a single soil parameter value. 
 

[[File:Fig3 GWY SoilMap.png|600px|Figure 3: Plynlimon Gwy Catchment Soil Types]] 

'''Figure 3: Plynlimon Gwy Catchment Soil Types''' 
 
For the purposes of this sensitivity analysis of the parameters a single soil type was used representing the general clay soil types that are present. 

== Green-Ampt Infiltration: User Parameters ==
Where the inbuilt USDA soil types, are not used, the user can specify their own values for the Suction, Hydraulic Conductivity, Porosity, Initial Soil Moisture and max ponding depth. What follows is a description of each parameter and the sensitivity to a 10% and 20% increase and decrease in the default values. 

== Capillary Suction Head ==
The suction head, represented in millimeters, is the capillary attraction on the soil voids. It is large for fine grain soils such as clays and smaller for sandy soils. 
To test the sensitivity of the simulated runoff at three gauged locations, as shown in figure 2, a low 49.5), mid representing the mean (184.4) and high (316.3) value of the suction head parameter was used with other parameters representing a clay soil (soil type 1). 
The larger the value the capillary suction head value, the more capillary action that is achieved and the amount of infiltration that takes place. This is shown by the increase in cumulative infiltration in the graph below with a greater cumulative infiltration for the 20% increase in the suction head. 

[[File:Fig4 sens to suction.png|600px|Figure 4: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Capillary Suction Head parameter in the Green-Ampt infiltration model.]] 

'''Figure 4: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Capillary Suction Head parameter in the Green-Ampt infiltration model.''' 
 
As a consequence of this, there is a less runoff generated as shown in Figure 5. The model is not particularly sensitive to the suction head parameter and this fits with observations made within the literature. 
 

[[File:Fig5 suctionhead.png|600px|Figure 5: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the Suction head parameter in the Green-Ampt infiltration model.]] 

'''Figure 5: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the Suction head parameter in the Green-Ampt infiltration model.''' 
 
''add comment on rising lim'' 
 
== Saturated Hydraulic Conductivity ==
The saturated hydraulic conductivity, measured in mm per hour, represents the ease that water can travel through the soil whilst it is saturated. The saturated hydraulic conductivity is the equivalent of the limiting infiltration rate in the Horton infiltration model. The hydraulic conductivity is high for sandy soils but low for compact clays. Again, the sensitivity was conducted by varying the value for clays, which itself is relatively by low, with three scenarios, low (0.3), mid representing the mean (15.86) and high (117.8). The results shown in figure 6 show that the parameter is very
sensitive to the changes in the hydraulic conductivity with the mid and high values providing a lot of infiltration and no runoff at the downstream gauge. As expected, the higher the hydraulic conductivity, then the more infiltration that occurs and the less runoff that is seen at the gauge. 
 

[[File:Fig6 hydroconduct.png|600px|Figure 6: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model when using a clay soil type.]] 

'''Figure 6: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model when using a clay soil type.''' 
 

[[File:Fig7 hydroconduct.png|600px|Figure 7: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model.]] 

'''Figure 7: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model.''' 

== Porosity ==
The porosity value represents the volume of dry voids per volume of soil and provides the maximum moisture deficit that is available, the difference between the moisture content at saturation and at the start of the simulation. 
Sandy soils tend to have lower porosities than clay soils, but drain to lower moisture contents between rainfall events because water is not held as strongly in the soil pores. Therefore, values of porosity tend to be higher for sandy soils when compared to clay soils. 
As shown in figure 8, the higher the porosity value, then the less runoff occurs due to increased infiltration although the model is not particular sensitive to the porosity value. 
 

[[File:Fig7 porosity sens.png|600px|Figure 8: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.]] 

'''Figure 8: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.''' 
 

[[File:Fig8 porosity sens.png|600px|Figure 9: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.]] 

'''Figure 9: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.''' 
 
== In built USDA soil type ==
The model was also run with the default in-build USDA soil types. Figure 10 shows the outputs. As expected the higher the soil type, then typically the more the infiltration and the lower the produced runoff. Soils 8-11, which represent sandy soils do not show any runoff in this example as the rainfall is all infiltrated. 
 
[[File:Fig10 GA soils.png|600px|Figure 10: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the USDA soil type parameter in the Green-Ampt infiltration model.]] 
'''Figure 10: Sensitivity of simulated flow at the Cefn-Brwn gauge locations in the Plynlimon Gwy catchment to the USDA soil type parameter in the Green-Ampt infiltration model.'''
 
== Summary ==
The Green-Ampt infiltration model is one of the available infiltration models within TUFLOW. There is a wide range of literature on Green-Ampt applications and some suggested parameter values for particular soil types, albeit soil types from the US. The 3 main Green-Ampt parameters have been tested to show the sensitivity of model outputs to the values as well as the variation in initial moisture. The results show that the model is relatively insensitive to the porosity value and suction head parameter. However, the outputs do show variations in runoff volume due to variations in both hydraulic conductivity and initial soil moisture. As part of any calibration exercise it is suggested that these would be the parameters that are most focused on. The hydraulic conductivity appears to affect the runoff volume throughout the event whereas the initial soil moisture has a limited impact at the beginning of the event before soils become saturated and results converge.

Green-Ampt Infiltration Parameters

2020-06-01T15:26:28Z

TuflowJoe:

== '''PAGE UNDER CONSTRUCTION''' ==

== Introduction ==
There are a number of methods available within TUFLOW to infiltrate water on the 2D surface into the sub-surface. These are Green-Ampt, Horton and Initial Loss/Continuing Loss. The models are used to represent hydrological losses particularly when direct rainfall is applied directly to the 2D surface and runoff is generated. As such, the infiltration module used, and the parameters selected, are important calibration parameters which should be used to calibrate simulated flows to observed flows. 

== Green-Ampt Infiltration ==
The Green-Ampt approach varies the rate of infiltration over time based on the soil’s hydraulic conductivity, suction, porosity and initial moisture content. The method assumes that as water begins to infiltrate the soil, a line developed differentiating between the ‘dry’ soil with moisture content θ_i) and the ‘wet’ soil (with moisture content equal to the porosity of the soil η). As the infiltrated water continues to move through the soil profile in a vertical direction, the soil moisture changes instantly from the initial content to a saturated state. This concept is shown in Figure 1. 
 

[[File:Fig_1_GA_Model.png|300px|Figure 1 Green-Ampt Model Concept]] 

'''Figure 1 Green-Ampt Model Concept''' 
''Figure courtesy of University of Texas''

 
The basic form of the Green-Ampt equation is expressed as follows: 
[[File:Basic_ga_equation.png|200px]] 

 
Where: 
:''t'' is time 
:K is the saturated hydraulic conductivity 
:∆''θ'' is defined as the soil capacity (the difference between the saturated and initial moisture content) 
:''φ'' is the soil suction head 
:h0 is the depth of ponded water 
:F(t) is the cumulative infiltration calculated from: 
[[File:Accumulative_infil.png|350px]] 

 
United States Department of Agriculture (USDA) soil types have been hardwired into TUFLOW and are presented in Table 1 along with the soil parameters. Alternatively, it is possible to define a customised soil type by specifying user defined values within the tsoilf. 
 
'''Table 1 USDA Soil types for the Green-Ampt Infiltration Method''' 
{| class="wikitable "
| '''USDA Soil Type''' || '''Suction (mm)''' || '''Hydraulic Conductivity (mm/hr)''' || '''Porosity (Fraction)'''
|-
| '''Clay''' || 316.3 || 0.3 || 0.385
|-
| '''Silty Clay''' || 292.2 || 0.5 || 0.423
|-
| '''Sandy Clay''' || 239 || 0.6 || 0.321
|-
| '''Clay Loam''' || 208.8 || 1 || 0.309
|-
| '''Silty Clay Loam''' || 273 || 1 || 0.432
|-
| '''Sandy Clay Loam''' || 218.5 || 1.5 || 0.33
|-
| '''Silt Loam''' || 166.8 || 3.4 || 0.486
|-
| '''Loam''' || 88.9 || 7.6 || 0.434
|-
| '''Sandy Loam''' || 110.1 || 10.9 || 0.412
|-
| '''Loamy Sand''' || 61.3 || 29.9 || 0.401
|-
| '''Sand''' || 49.5 || 117.8 || 0.417
|}
 
Table 2 presents summary statistics for the Green-Ampt Parameters types. 
 
'''Table 2 USDA Summary Statistics for all Soil types for the Green-Ampt Infiltration Method''' 
{| class="wikitable "
| '''Stat''' || '''Suction (mm)''' || '''Hydraulic Conductivity (mm/hr)''' || '''Porosity (Fraction)'''
|-
| '''Min''' || 49.5 || 0.3 || 0.31
|-
| '''Max''' || 316.3 || 117.8 || 0.49
|-
| '''Mean''' || 184.04 || 15.86 || 0.4
|-
| '''SD''' || 94.82 || 34.92 || 0.05
|}
 
The comparison has been undertaken on a real-world model of the Plynlimon catchment in mid-Wales. The model was run with a real rainfall event from 2015 with a temporal resolution of 30 minutes as shown in the Figure 2. 
 

[[File:Fig2 GWY RF.png|600px|Figure 2: Plynlimon Rainfall]] 

'''Figure 2: Plynlimon Rainfall''' 
Within the Plynlimon catchment, the soil types were distributed within the catchment as shown in the map below although a single soil type has been selected for the comparison. Therefore, the effect seen is a consequence of changing a single soil parameter value. 
 

[[File:Fig3 GWY SoilMap.png|600px|Figure 3: Plynlimon Gwy Catchment Soil Types]] 

'''Figure 3: Plynlimon Gwy Catchment Soil Types''' 
 
For the purposes of this sensitivity analysis of the parameters a single soil type was used representing the general clay soil types that are present. 

== Green-Ampt Infiltration: User Parameters ==
Where the inbuilt USDA soil types, are not used, the user can specify their own values for the Suction, Hydraulic Conductivity, Porosity, Initial Soil Moisture and max ponding depth. What follows is a description of each parameter and the sensitivity to a 10% and 20% increase and decrease in the default values. 

== Capillary Suction Head ==
The suction head, represented in millimeters, is the capillary attraction on the soil voids. It is large for fine grain soils such as clays and smaller for sandy soils. 
To test the sensitivity of the simulated runoff at three gauged locations, as shown in figure 2, a low 49.5), mid representing the mean (184.4) and high (316.3) value of the suction head parameter was used with other parameters representing a clay soil (soil type 1). 
The larger the value the capillary suction head value, the more capillary action that is achieved and the amount of infiltration that takes place. This is shown by the increase in cumulative infiltration in the graph below with a greater cumulative infiltration for the 20% increase in the suction head. 

[[File:Fig4 sens to suction.png|600px|Figure 4: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Capillary Suction Head parameter in the Green-Ampt infiltration model.]] 

'''Figure 4: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Capillary Suction Head parameter in the Green-Ampt infiltration model.''' 
 
As a consequence of this, there is a less runoff generated as shown in Figure 5. The model is not particularly sensitive to the suction head parameter and this fits with observations made within the literature. 
 

[[File:Fig5 suctionhead.png|600px|Figure 5: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the Suction head parameter in the Green-Ampt infiltration model.]] 

'''Figure 5: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the Suction head parameter in the Green-Ampt infiltration model.''' 
 
''add comment on rising lim'' 
 
== Saturated Hydraulic Conductivity ==
The saturated hydraulic conductivity, measured in mm per hour, represents the ease that water can travel through the soil whilst it is saturated. The saturated hydraulic conductivity is the equivalent of the limiting infiltration rate in the Horton infiltration model. The hydraulic conductivity is high for sandy soils but low for compact clays. Again, the sensitivity was conducted by varying the value for clays, which itself is relatively by low, with three scenarios, low (0.3), mid representing the mean (15.86) and high (117.8). The results shown in figure 6 show that the parameter is very
sensitive to the changes in the hydraulic conductivity with the mid and high values providing a lot of infiltration and no runoff at the downstream gauge. As expected, the higher the hydraulic conductivity, then the more infiltration that occurs and the less runoff that is seen at the gauge. 
 

[[File:Fig6 hydroconduct.png|600px|Figure 6: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model when using a clay soil type.]] 

'''Figure 6: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model when using a clay soil type.''' 
 

[[File:Fig7 hydroconduct.png|600px|Figure 7: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model.]] 

'''Figure 7: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model.''' 

== Porosity ==
The porosity value represents the volume of dry voids per volume of soil and provides the maximum moisture deficit that is available, the difference between the moisture content at saturation and at the start of the simulation. 
Sandy soils tend to have lower porosities than clay soils, but drain to lower moisture contents between rainfall events because water is not held as strongly in the soil pores. Therefore, values of porosity tend to be higher for sandy soils when compared to clay soils. 
As shown in figure 8, the higher the porosity value, then the less runoff occurs due to increased infiltration although the model is not particular sensitive to the porosity value. 
 

[[File:Fig7 porosity sens.png|600px|Figure 8: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.]] 

'''Figure 8: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.''' 
 

[[File:Fig8 porosity sens.png|600px|Figure 9: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.]] 

'''Figure 9: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.''' 
 
== In built USDA soil type ==
The model was also run with the default in-build USDA soil types. Figure 10 shows the outputs. As expected the higher the soil type, then typically the more the infiltration and the lower the produced runoff. Soils 8-11, which represent sandy soils do not show any runoff in this example as the rainfall is all infiltrated. 
 
[[File:Fig10 GA soils.png|600px|Figure 10: Sensitivity of simulated flow at the Cefn-Brwn gauge location in the Plynlimon Gwy catchment to the USDA soil type parameter in the Green-Ampt infiltration model.]] 
'''Figure 10: Sensitivity of simulated flow at the Cefn-Brwn gauge locations in the Plynlimon Gwy catchment to the USDA soil type parameter in the Green-Ampt infiltration model.'''
 
== Summary ==
The Green-Ampt infiltration model is one of the available infiltration models within TUFLOW. There is a wide range of literature on Green-Ampt applications and some suggested parameter values for particular soil types, albeit soil types from the US. The 3 main Green-Ampt parameters have been tested to show the sensitivity of model outputs to the values as well as the variation in initial moisture. The results show that the model is relatively insensitive to the porosity value and suction head parameter. However, the outputs do show variations in runoff volume due to variations in both hydraulic conductivity and initial soil moisture. As part of any calibration exercise it is suggested that these would be the parameters that are most focused on. The hydraulic conductivity appears to affect the runoff volume throughout the event whereas the initial soil moisture has a limited impact at the beginning of the event before soils become saturated and results converge.

Green-Ampt Infiltration Parameters

2020-06-01T15:14:17Z

TuflowJoe:

== '''PAGE UNDER CONSTRUCTION''' ==

== Introduction ==
There are a number of methods available within TUFLOW to infiltrate water on the 2D surface into the sub-surface. These are Green-Ampt, Horton and Initial Loss/Continuing Loss. The models are used to represent hydrological losses particularly when direct rainfall is applied directly to the 2D surface and runoff is generated. As such, the infiltration module used, and the parameters selected, are important calibration parameters which should be used to calibrate simulated flows to observed flows. 

== Green-Ampt Infiltration ==
The Green-Ampt approach varies the rate of infiltration over time based on the soil’s hydraulic conductivity, suction, porosity and initial moisture content. The method assumes that as water begins to infiltrate the soil, a line developed differentiating between the ‘dry’ soil with moisture content θ_i) and the ‘wet’ soil (with moisture content equal to the porosity of the soil η). As the infiltrated water continues to move through the soil profile in a vertical direction, the soil moisture changes instantly from the initial content to a saturated state. This concept is shown in Figure 1. 
 

[[File:Fig_1_GA_Model.png|300px|Figure 1 Green-Ampt Model Concept]] 

'''Figure 1 Green-Ampt Model Concept''' 
''Figure courtesy of University of Texas''

 
The basic form of the Green-Ampt equation is expressed as follows: 
[[File:Basic_ga_equation.png|200px]] 

 
Where: 
:''t'' is time 
:K is the saturated hydraulic conductivity 
:∆''θ'' is defined as the soil capacity (the difference between the saturated and initial moisture content) 
:''φ'' is the soil suction head 
:h0 is the depth of ponded water 
:F(t) is the cumulative infiltration calculated from: 
[[File:Accumulative_infil.png|350px]] 

 
United States Department of Agriculture (USDA) soil types have been hardwired into TUFLOW and are presented in Table 1 along with the soil parameters. Alternatively, it is possible to define a customised soil type by specifying user defined values within the tsoilf. 
 
'''Table 1 USDA Soil types for the Green-Ampt Infiltration Method''' 
{| class="wikitable "
| '''USDA Soil Type''' || '''Suction (mm)''' || '''Hydraulic Conductivity (mm/hr)''' || '''Porosity (Fraction)'''
|-
| '''Clay''' || 316.3 || 0.3 || 0.385
|-
| '''Silty Clay''' || 292.2 || 0.5 || 0.423
|-
| '''Sandy Clay''' || 239 || 0.6 || 0.321
|-
| '''Clay Loam''' || 208.8 || 1 || 0.309
|-
| '''Silty Clay Loam''' || 273 || 1 || 0.432
|-
| '''Sandy Clay Loam''' || 218.5 || 1.5 || 0.33
|-
| '''Silt Loam''' || 166.8 || 3.4 || 0.486
|-
| '''Loam''' || 88.9 || 7.6 || 0.434
|-
| '''Sandy Loam''' || 110.1 || 10.9 || 0.412
|-
| '''Loamy Sand''' || 61.3 || 29.9 || 0.401
|-
| '''Sand''' || 49.5 || 117.8 || 0.417
|}
 
Table 2 presents summary statistics for the Green-Ampt Parameters types. 
 
'''Table 2 USDA Summary Statistics for all Soil types for the Green-Ampt Infiltration Method''' 
{| class="wikitable "
| '''Stat''' || '''Suction (mm)''' || '''Hydraulic Conductivity (mm/hr)''' || '''Porosity (Fraction)'''
|-
| '''Min''' || 49.5 || 0.3 || 0.31
|-
| '''Max''' || 316.3 || 117.8 || 0.49
|-
| '''Mean''' || 184.04 || 15.86 || 0.4
|-
| '''SD''' || 94.82 || 34.92 || 0.05
|}
 
The comparison has been undertaken on a real-world model of the Plynlimon catchment in mid-Wales. The model was run with a real rainfall event from 2015 with a temporal resolution of 30 minutes as shown in the Figure 2. 
 

[[File:Fig2 GWY RF.png|600px|Figure 2: Plynlimon Rainfall]] 

'''Figure 2: Plynlimon Rainfall''' 
Within the Plynlimon catchment, the soil types were distributed within the catchment as shown in the map below although a single soil type has been selected for the comparison. Therefore, the effect seen is a consequence of changing a single soil parameter value. 
 

[[File:Fig3 GWY SoilMap.png|600px|Figure 3: Plynlimon Gwy Catchment Soil Types]] 

'''Figure 3: Plynlimon Gwy Catchment Soil Types''' 
 
For the purposes of this sensitivity analysis of the parameters a single soil type was used representing the general clay soil types that are present. 

== Green-Ampt Infiltration: User Parameters ==
Where the inbuilt USDA soil types, are not used, the user can specify their own values for the Suction, Hydraulic Conductivity, Porosity, Initial Soil Moisture and max ponding depth. What follows is a description of each parameter and the sensitivity to a 10% and 20% increase and decrease in the default values. 

== Capillary Suction Head ==
The suction head, represented in millimeters, is the capillary attraction on the soil voids. It is large for fine grain soils such as clays and smaller for sandy soils. 
To test the sensitivity of the simulated runoff at three gauged locations, as shown in figure 2, a low 49.5), mid representing the mean (184.4) and high (316.3) value of the suction head parameter was used with other parameters representing a clay soil (soil type 1). 
The larger the value the capillary suction head value, the more capillary action that is achieved and the amount of infiltration that takes place. This is shown by the increase in cumulative infiltration in the graph below with a greater cumulative infiltration for the 20% increase in the suction head. 

[[File:Fig4 sens to suction.png|600px|Figure 4: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Capillary Suction Head parameter in the Green-Ampt infiltration model.]] 

'''Figure 4: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Capillary Suction Head parameter in the Green-Ampt infiltration model.''' 
 
As a consequence of this, there is a less runoff generated as shown in Figure 5. The model is not particularly sensitive to the suction head parameter and this fits with observations made within the literature. 
 

[[File:Fig5 suctionhead.png|600px|Figure 5: Sensitivity of simulated flow at the Cefn-Brwn gauge locations in the Plynlimon Gwy catchment to the Suction head parameter in the Green-Ampt infiltration model.]] 

'''Figure 5: Sensitivity of simulated flow at the Cefn-Brwn gauge locations in the Plynlimon Gwy catchment to the Suction head parameter in the Green-Ampt infiltration model.''' 
 
''add comment on rising lim'' 
 
== Saturated Hydraulic Conductivity ==
The saturated hydraulic conductivity, measured in mm per hour, represents the ease that water can travel through the soil whilst it is saturated. The saturated hydraulic conductivity is the equivalent of the limiting infiltration rate in the Horton infiltration model. The hydraulic conductivity is high for sandy soils but low for compact clays. Again, the sensitivity was conducted by varying the value for clays, which itself is relatively by low, with three scenarios, low (0.3), mid representing the mean (15.86) and high (117.8). The results shown in figure 6 show that the parameter is very
sensitive to the changes in the hydraulic conductivity with the mid and high values providing a lot of infiltration and no runoff at the downstream gauge. As expected, the higher the hydraulic conductivity, then the more infiltration that occurs and the less runoff that is seen at the gauge. 
 

[[File:Fig6 hydroconduct.png|600px|Figure 6: Sensitivity of simulated flow at 3 gauge locations in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model when using a clay soil type.]] 

'''Figure 6: Sensitivity of simulated flow at 3 gauge locations in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model when using a clay soil type.''' 
 

[[File:Fig7 hydroconduct.png|600px|Figure 7: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model.]] 

'''Figure 7: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model.''' 

== Porosity ==
The porosity value represents the volume of dry voids per volume of soil and provides the maximum moisture deficit that is available, the difference between the moisture content at saturation and at the start of the simulation. 
Sandy soils tend to have lower porosities than clay soils, but drain to lower moisture contents between rainfall events because water is not held as strongly in the soil pores. Therefore, values of porosity tend to be higher for sandy soils when compared to clay soils. 
As shown in figure 8, the higher the porosity value, then the less runoff occurs due to increased infiltration although the model is not particular sensitive to the porosity value. 
 

[[File:Fig7 porosity sens.png|600px|Figure 8: Sensitivity of simulated flow at 3 gauge locations in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.]] 

'''Figure 8: Sensitivity of simulated flow at 3 gauge locations in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.''' 
 

[[File:Fig8 porosity sens.png|600px|Figure 9: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.]] 

'''Figure 9: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.''' 
 
== In built USDA soil type ==
The model was also run with the default in-build USDA soil types. Figure 10 shows the outputs. As expected the higher the soil type, then typically the more the infiltration and the lower the produced runoff. Soils 8-11, which represent sandy soils do not show any runoff in this example as the rainfall is all infiltrated. 
 
[[File:Fig10 GA soils.png|600px|Figure 10: Sensitivity of simulated flow at the Cefn-Brwn gauge locations in the Plynlimon Gwy catchment to the USDA soil type parameter in the Green-Ampt infiltration model.]] 
'''Figure 10: Sensitivity of simulated flow at the Cefn-Brwn gauge locations in the Plynlimon Gwy catchment to the USDA soil type parameter in the Green-Ampt infiltration model.'''
 
== Summary ==
The Green-Ampt infiltration model is one of the available infiltration models within TUFLOW. There is a wide range of literature on Green-Ampt applications and some suggested parameter values for particular soil types, albeit soil types from the US. The 3 main Green-Ampt parameters have been tested to show the sensitivity of model outputs to the values as well as the variation in initial moisture. The results show that the model is relatively insensitive to the porosity value and suction head parameter. However, the outputs do show variations in runoff volume due to variations in both hydraulic conductivity and initial soil moisture. As part of any calibration exercise it is suggested that these would be the parameters that are most focused on. The hydraulic conductivity appears to affect the runoff volume throughout the event whereas the initial soil moisture has a limited impact at the beginning of the event before soils become saturated and results converge.

Green-Ampt Infiltration Parameters

2020-06-01T15:10:01Z

TuflowJoe:

== '''PAGE UNDER CONSTRUCTION''' ==

== Introduction ==
There are a number of methods available within TUFLOW to infiltrate water on the 2D surface into the sub-surface. These are Green-Ampt, Horton and Initial Loss/Continuing Loss. The models are used to represent hydrological losses particularly when direct rainfall is applied directly to the 2D surface and runoff is generated. As such, the infiltration module used, and the parameters selected, are important calibration parameters which should be used to calibrate simulated flows to observed flows. 

== Green-Ampt Infiltration ==
The Green-Ampt approach varies the rate of infiltration over time based on the soil’s hydraulic conductivity, suction, porosity and initial moisture content. The method assumes that as water begins to infiltrate the soil, a line developed differentiating between the ‘dry’ soil with moisture content θ_i) and the ‘wet’ soil (with moisture content equal to the porosity of the soil η). As the infiltrated water continues to move through the soil profile in a vertical direction, the soil moisture changes instantly from the initial content to a saturated state. This concept is shown in Figure 1. 
 

[[File:Fig_1_GA_Model.png|300px|Figure 1 Green-Ampt Model Concept]] 

'''Figure 1 Green-Ampt Model Concept''' 
''Figure courtesy of University of Texas''

 
The basic form of the Green-Ampt equation is expressed as follows: 
[[File:Basic_ga_equation.png|200px]] 

 
Where: 
:''t'' is time 
:K is the saturated hydraulic conductivity 
:∆''θ'' is defined as the soil capacity (the difference between the saturated and initial moisture content) 
:''φ'' is the soil suction head 
:h0 is the depth of ponded water 
:F(t) is the cumulative infiltration calculated from: 
[[File:Accumulative_infil.png|350px]] 

 
United States Department of Agriculture (USDA) soil types have been hardwired into TUFLOW and are presented in Table 1 along with the soil parameters. Alternatively, it is possible to define a customised soil type by specifying user defined values within the tsoilf. 
 
'''Table 1 USDA Soil types for the Green-Ampt Infiltration Method''' 
{| class="wikitable "
| '''USDA Soil Type''' || '''Suction (mm)''' || '''Hydraulic Conductivity (mm/hr)''' || '''Porosity (Fraction)'''
|-
| '''Clay''' || 316.3 || 0.3 || 0.385
|-
| '''Silty Clay''' || 292.2 || 0.5 || 0.423
|-
| '''Sandy Clay''' || 239 || 0.6 || 0.321
|-
| '''Clay Loam''' || 208.8 || 1 || 0.309
|-
| '''Silty Clay Loam''' || 273 || 1 || 0.432
|-
| '''Sandy Clay Loam''' || 218.5 || 1.5 || 0.33
|-
| '''Silt Loam''' || 166.8 || 3.4 || 0.486
|-
| '''Loam''' || 88.9 || 7.6 || 0.434
|-
| '''Sandy Loam''' || 110.1 || 10.9 || 0.412
|-
| '''Loamy Sand''' || 61.3 || 29.9 || 0.401
|-
| '''Sand''' || 49.5 || 117.8 || 0.417
|}
 
Table 2 presents summary statistics for the Green-Ampt Parameters types. 
 
'''Table 2 USDA Summary Statistics for all Soil types for the Green-Ampt Infiltration Method''' 
{| class="wikitable "
| '''Stat''' || '''Suction (mm)''' || '''Hydraulic Conductivity (mm/hr)''' || '''Porosity (Fraction)'''
|-
| '''Min''' || 49.5 || 0.3 || 0.31
|-
| '''Max''' || 316.3 || 117.8 || 0.49
|-
| '''Mean''' || 184.04 || 15.86 || 0.4
|-
| '''SD''' || 94.82 || 34.92 || 0.05
|}
 
The comparison has been undertaken on a real-world model of the Plynlimon catchment in mid-Wales. The model was run with a real rainfall event from 2015 with a temporal resolution of 30 minutes as shown in the Figure 2. 
 

[[File:Fig2 GWY RF.png|600px|Figure 2: Plynlimon Rainfall]] 

'''Figure 2: Plynlimon Rainfall''' 
Within the Plynlimon catchment, the soil types were distributed within the catchment as shown in the map below although a single soil type has been selected for the comparison. Therefore, the effect seen is a consequence of changing a single soil parameter value. 
 

[[File:Fig3 GWY SoilMap.png|600px|Figure 3: Plynlimon Gwy Catchment Soil Types]] 

'''Figure 3: Plynlimon Gwy Catchment Soil Types''' 
 
For the purposes of this sensitivity analysis of the parameters a single soil type was used representing the general clay soil types that are present. 

== Green-Ampt Infiltration: User Parameters ==
Where the inbuilt USDA soil types, are not used, the user can specify their own values for the Suction, Hydraulic Conductivity, Porosity, Initial Soil Moisture and max ponding depth. What follows is a description of each parameter and the sensitivity to a 10% and 20% increase and decrease in the default values. 

== Capillary Suction Head ==
The suction head, represented in millimeters, is the capillary attraction on the soil voids. It is large for fine grain soils such as clays and smaller for sandy soils. 
To test the sensitivity of the simulated runoff at three gauged locations, as shown in figure 2, a low 49.5), mid representing the mean (184.4) and high (316.3) value of the suction head parameter was used with other parameters representing a clay soil (soil type 1). 
The larger the value the capillary suction head value, the more capillary action that is achieved and the amount of infiltration that takes place. This is shown by the increase in cumulative infiltration in the graph below with a greater cumulative infiltration for the 20% increase in the suction head. 

[[File:Fig4 sens to suction.png|600px|Figure 4: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Capillary Suction Head parameter in the Green-Ampt infiltration model.]] 

'''Figure 4: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Capillary Suction Head parameter in the Green-Ampt infiltration model.''' 
 
As a consequence of this, there is a less runoff generated as shown in Figure 5. The model is not particularly sensitive to the suction head parameter and this fits with observations made within the literature. 
 

[[File:Fig5 suctionhead.png|600px|Figure 5: Sensitivity of simulated flow at the Cefn-Brwn gauge locations in the Plynlimon Gwy catchment to the Suction head parameter in the Green-Ampt infiltration model.]] 

'''Figure 5: Sensitivity of simulated flow at the Cefn-Brwn gauge locations in the Plynlimon Gwy catchment to the Suction head parameter in the Green-Ampt infiltration model.''' 
 
''add comment on rising lim'' 
 
== Saturated Hydraulic Conductivity ==
The saturated hydraulic conductivity, measured in mm per hour, represents the ease that water can travel through the soil whilst it is saturated. The saturated hydraulic conductivity is the equivalent of the limiting infiltration rate in the Horton infiltration model. The hydraulic conductivity is high for sandy soils but low for compact clays. Again, the sensitivity was conducted by varying the value for clays, which itself is relatively by low, with three scenarios, low (0.3), mid representing the mean (15.86) and high (117.8). The results shown in figure 6 show that the parameter is very
sensitive to the changes in the hydraulic conductivity with the mid and high values providing a lot of infiltration and no runoff at the downstream gauge. As expected, the higher the hydraulic conductivity, then the more infiltration that occurs and the less runoff that is seen at the gauge. 
 

[[File:Fig6 hydroconduct.png|600px|Figure 6: Sensitivity of simulated flow at 3 gauge locations in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model when using a clay soil type.]] 

'''Figure 6: Sensitivity of simulated flow at 3 gauge locations in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model when using a clay soil type.''' 
 

[[File:Fig7 hydroconduct.png|600px|Figure 7: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model.]] 

'''Figure 7: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model.''' 

== Porosity ==
The porosity value represents the volume of dry voids per volume of soil and provides the maximum moisture deficit that is available, the difference between the moisture content at saturation and at the start of the simulation. 
Sandy soils tend to have lower porosities than clay soils, but drain to lower moisture contents between rainfall events because water is not held as strongly in the soil pores. Therefore, values of porosity tend to be higher for sandy soils when compared to clay soils. 
As shown in figure 8, the higher the porosity value, then the less runoff occurs due to increased infiltration although the model is not particular sensitive to the porosity value. 
 

[[File:Fig7 porosity sens.png|600px|Figure 8: Sensitivity of simulated flow at 3 gauge locations in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.]] 

'''Figure 8: Sensitivity of simulated flow at 3 gauge locations in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.''' 
 

[[File:Fig8 porosity sens.png|600px|Figure 9: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.]] 

'''Figure 9: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.''' 
 
== In built USDA soil type ==
The model was also run with the default in-build USDA soil types. Figure 10 shows the outputs. As expected the higher the soil type, then typically the more the infiltration and the lower the produced runoff. Soils 8-11, which represent sandy soils to not show any runoff in this example as the rainfall is all infiltrated. 
 
[[File:Fig10 GA soils.png|600px|Figure 8: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.]] 
'''Figure Caption NEEDED'''
 
== Summary ==
The Green-Ampt infiltration model is one of the available infiltration models within TUFLOW. There is a wide range of literature on Green-Ampt applications and some suggested parameter values for particular soil types, albeit soil types from the US. The 3 main Green-Ampt parameters have been tested to show the sensitivity of model outputs to the values as well as the variation in initial moisture. The results show that the model is relatively insensitive to the porosity value and suction head parameter. However, the outputs do show variations in runoff volume due to variations in both hydraulic conductivity and initial soil moisture. As part of any calibration exercise it is suggested that these would be the parameters that are most focused on. The hydraulic conductivity appears to affect the runoff volume throughout the event whereas the initial soil moisture has a limited impact at the beginning of the event before soils become saturated and results converge.

Green-Ampt Infiltration Parameters

2020-06-01T15:03:45Z

TuflowJoe:

== '''PAGE UNDER CONSTRUCTION''' ==

== Introduction ==
There are a number of methods available within TUFLOW to infiltrate water on the 2D surface into the sub-surface. These are Green-Ampt, Horton and Initial Loss/Continuing Loss. The models are used to represent hydrological losses particularly when direct rainfall is applied directly to the 2D surface and runoff is generated. As such, the infiltration module used, and the parameters selected, are important calibration parameters which should be used to calibrate simulated flows to observed flows. 

== Green-Ampt Infiltration ==
The Green-Ampt approach varies the rate of infiltration over time based on the soil’s hydraulic conductivity, suction, porosity and initial moisture content. The method assumes that as water begins to infiltrate the soil, a line developed differentiating between the ‘dry’ soil with moisture content θ_i) and the ‘wet’ soil (with moisture content equal to the porosity of the soil η). As the infiltrated water continues to move through the soil profile in a vertical direction, the soil moisture changes instantly from the initial content to a saturated state. This concept is shown in Figure 1. 
 

[[File:Fig_1_GA_Model.png|300px|Figure 1 Green-Ampt Model Concept]] 

'''Figure 1 Green-Ampt Model Concept''' 
''Figure courtesy of University of Texas''

 
The basic form of the Green-Ampt equation is expressed as follows: 
[[File:Basic_ga_equation.png|200px]] 

 
Where: 
:''t'' is time 
:K is the saturated hydraulic conductivity 
:∆''θ'' is defined as the soil capacity (the difference between the saturated and initial moisture content) 
:''φ'' is the soil suction head 
:h0 is the depth of ponded water 
:F(t) is the cumulative infiltration calculated from: 
[[File:Accumulative_infil.png|350px]] 

 
United States Department of Agriculture (USDA) soil types have been hardwired into TUFLOW and are presented in Table 1 along with the soil parameters. Alternatively, it is possible to define a customised soil type by specifying user defined values within the tsoilf. 
 
'''Table 1 USDA Soil types for the Green-Ampt Infiltration Method''' 
{| class="wikitable "
| '''USDA Soil Type''' || '''Suction (mm)''' || '''Hydraulic Conductivity (mm/hr)''' || '''Porosity (Fraction)'''
|-
| '''Clay''' || 316.3 || 0.3 || 0.385
|-
| '''Silty Clay''' || 292.2 || 0.5 || 0.423
|-
| '''Sandy Clay''' || 239 || 0.6 || 0.321
|-
| '''Clay Loam''' || 208.8 || 1 || 0.309
|-
| '''Silty Clay Loam''' || 273 || 1 || 0.432
|-
| '''Sandy Clay Loam''' || 218.5 || 1.5 || 0.33
|-
| '''Silt Loam''' || 166.8 || 3.4 || 0.486
|-
| '''Loam''' || 88.9 || 7.6 || 0.434
|-
| '''Sandy Loam''' || 110.1 || 10.9 || 0.412
|-
| '''Loamy Sand''' || 61.3 || 29.9 || 0.401
|-
| '''Sand''' || 49.5 || 117.8 || 0.417
|}
 
Table 2 presents summary statistics for the Green-Ampt Parameters types. 
 
'''Table 2 USDA Summary Statistics for all Soil types for the Green-Ampt Infiltration Method''' 
{| class="wikitable "
| '''Stat''' || '''Suction (mm)''' || '''Hydraulic Conductivity (mm/hr)''' || '''Porosity (Fraction)'''
|-
| '''Min''' || 49.5 || 0.3 || 0.31
|-
| '''Max''' || 316.3 || 117.8 || 0.49
|-
| '''Mean''' || 184.04 || 15.86 || 0.4
|-
| '''SD''' || 94.82 || 34.92 || 0.05
|}
 
The comparison has been undertaken on a real-world model of the Plynlimon catchment in mid-Wales. The model was run with a real rainfall event from 2015 with a temporal resolution of 30 minutes as shown in the Figure 2. 
 

[[File:Fig2 GWY RF.png|600px|Figure 2: Plynlimon Rainfall]] 

'''Figure 2: Plynlimon Rainfall''' 
Within the Plynlimon catchment, the soil types were distributed within the catchment as shown in the map below although a single soil type has been selected for the comparison. Therefore, the effect seen is a consequence of changing a single soil parameter value. 
 

[[File:Fig3 GWY SoilMap.png|600px|Figure 3: Plynlimon Gwy Catchment Soil Types]] 

'''Figure 3: Plynlimon Gwy Catchment Soil Types''' 
 
For the purposes of this sensitivity analysis of the parameters a single soil type was used representing the general clay soil types that are present. 

== Green-Ampt Infiltration: User Parameters ==
Where the inbuilt USDA soil types, are not used, the user can specify their own values for the Suction, Hydraulic Conductivity, Porosity, Initial Soil Moisture and max ponding depth. What follows is a description of each parameter and the sensitivity to a 10% and 20% increase and decrease in the default values. 

== Capillary Suction Head ==
The suction head, represented in millimeters, is the capillary attraction on the soil voids. It is large for fine grain soils such as clays and smaller for sandy soils. 
To test the sensitivity of the simulated runoff at three gauged locations, as shown in figure 2, a low 49.5), mid representing the mean (184.4) and high (316.3) value of the suction head parameter was used with other parameters representing a clay soil (soil type 1). 
The larger the value the capillary suction head value, the more capillary action that is achieved and the amount of infiltration that takes place. This is shown by the increase in cumulative infiltration in the graph below with a greater cumulative infiltration for the 20% increase in the suction head. 

[[File:Fig4 sens to suction.png|600px|Figure 4: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Capillary Suction Head parameter in the Green-Ampt infiltration model.]] 

'''Figure 4: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Capillary Suction Head parameter in the Green-Ampt infiltration model.''' 
 
As a consequence of this, there is a less runoff generated as shown in Figure 5. The model is not particularly sensitive to the suction head parameter and this fits with observations made within the literature. 
 

[[File:Fig5 suctionhead.png|600px|Figure 5: Sensitivity of simulated flow at the Cefn-Brwn gauge locations in the Plynlimon Gwy catchment to the Suction head parameter in the Green-Ampt infiltration model.]] 

'''Figure 5: Sensitivity of simulated flow at the Cefn-Brwn gauge locations in the Plynlimon Gwy catchment to the Suction head parameter in the Green-Ampt infiltration model.''' 
 
''add comment on rising lim'' 
 
== Saturated Hydraulic Conductivity ==
The saturated hydraulic conductivity, measured in mm per hour, represents the ease that water can travel through the soil whilst it is saturated. The saturated hydraulic conductivity is the equivalent of the limiting infiltration rate in the Horton infiltration model. The hydraulic conductivity is high for sandy soils but low for compact clays. Again, the sensitivity was conducted by varying the value for clays, which itself is relatively by low, with three scenarios, low (0.3), mid representing the mean (15.86) and high (117.8). The results shown in figure 6 show that the parameter is very
sensitive to the changes in the hydraulic conductivity with the mid and high values providing a lot of infiltration and no runoff at the downstream gauge. As expected, the higher the hydraulic conductivity, then the more infiltration that occurs and the less runoff that is seen at the gauge. 
 

[[File:Fig6 hydroconduct.png|600px|Figure 6: Sensitivity of simulated flow at 3 gauge locations in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model when using a clay soil type.]] 

'''Figure 6: Sensitivity of simulated flow at 3 gauge locations in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model when using a clay soil type.''' 
 

[[File:Fig7 hydroconduct.png|600px|Figure 7: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model.]] 

'''Figure 7: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model.''' 

== Porosity ==
The porosity value represents the volume of dry voids per volume of soil and provides the maximum moisture deficit that is available, the difference between the moisture content at saturation and at the start of the simulation. 
Sandy soils tend to have lower porosities than clay soils, but drain to lower moisture contents between rainfall events because water is not held as strongly in the soil pores. Therefore, values of porosity tend to be higher for sandy soils when compared to clay soils. 
As shown in figure 8, the higher the porosity value, then the less runoff occurs due to increased infiltration although the model is not particular sensitive to the porosity value. 
 

[[File:Fig7 porosity sens.png|600px|Figure 8: Sensitivity of simulated flow at 3 gauge locations in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.]] 

'''Figure 8: Sensitivity of simulated flow at 3 gauge locations in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.''' 
 

[[File:Fig8 porosity sens.png|600px|Figure 9: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.]] 

'''Figure 9: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.''' 
 
== In built USDA soil type ==
The model was also run with the default in-build USDA soil types. Figure 11 shows the outputs. As expected the higher the soil type, then typically the more the infiltration and the lower the produced runoff. Soils 8-11, which represent sandy soils to not show any runoff in this example as the rainfall is all infiltrated. 
 
[[File:Fig10 GA soils.png|600px|Figure 8: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.]] 
'''Figure Caption NEEDED'''
 
== Summary ==
The Green-Ampt infiltration model is one of the available infiltration models within TUFLOW. There is a wide range of literature on Green-Ampt applications and some suggested parameter values for particular soil types, albeit soil types from the US. The 3 main Green-Ampt parameters have been tested to show the sensitivity of model outputs to the values as well as the variation in initial moisture. The results show that the model is relatively insensitive to the porosity value and suction head parameter. However, the outputs do show variations in runoff volume due to variations in both hydraulic conductivity and initial soil moisture. As part of any calibration exercise it is suggested that these would be the parameters that are most focused on. The hydraulic conductivity appears to affect the runoff volume throughout the event whereas the initial soil moisture has a limited impact at the beginning of the event before soils become saturated and results converge.

File:Fig10 GA soils.png

2020-06-01T15:02:16Z

TuflowJoe:

Green-Ampt Infiltration Parameters

2020-06-01T15:00:24Z

TuflowJoe:

== '''PAGE UNDER CONSTRUCTION''' ==

== Introduction ==
There are a number of methods available within TUFLOW to infiltrate water on the 2D surface into the sub-surface. These are Green-Ampt, Horton and Initial Loss/Continuing Loss. The models are used to represent hydrological losses particularly when direct rainfall is applied directly to the 2D surface and runoff is generated. As such, the infiltration module used, and the parameters selected, are important calibration parameters which should be used to calibrate simulated flows to observed flows. 

== Green-Ampt Infiltration ==
The Green-Ampt approach varies the rate of infiltration over time based on the soil’s hydraulic conductivity, suction, porosity and initial moisture content. The method assumes that as water begins to infiltrate the soil, a line developed differentiating between the ‘dry’ soil with moisture content θ_i) and the ‘wet’ soil (with moisture content equal to the porosity of the soil η). As the infiltrated water continues to move through the soil profile in a vertical direction, the soil moisture changes instantly from the initial content to a saturated state. This concept is shown in Figure 1. 
 

[[File:Fig_1_GA_Model.png|300px|Figure 1 Green-Ampt Model Concept]] 

'''Figure 1 Green-Ampt Model Concept''' 
''Figure courtesy of University of Texas''

 
The basic form of the Green-Ampt equation is expressed as follows: 
[[File:Basic_ga_equation.png|200px]] 

 
Where: 
:''t'' is time 
:K is the saturated hydraulic conductivity 
:∆''θ'' is defined as the soil capacity (the difference between the saturated and initial moisture content) 
:''φ'' is the soil suction head 
:h0 is the depth of ponded water 
:F(t) is the cumulative infiltration calculated from: 
[[File:Accumulative_infil.png|350px]] 

 
United States Department of Agriculture (USDA) soil types have been hardwired into TUFLOW and are presented in Table 1 along with the soil parameters. Alternatively, it is possible to define a customised soil type by specifying user defined values within the tsoilf. 
 
'''Table 1 USDA Soil types for the Green-Ampt Infiltration Method''' 
{| class="wikitable "
| '''USDA Soil Type''' || '''Suction (mm)''' || '''Hydraulic Conductivity (mm/hr)''' || '''Porosity (Fraction)'''
|-
| '''Clay''' || 316.3 || 0.3 || 0.385
|-
| '''Silty Clay''' || 292.2 || 0.5 || 0.423
|-
| '''Sandy Clay''' || 239 || 0.6 || 0.321
|-
| '''Clay Loam''' || 208.8 || 1 || 0.309
|-
| '''Silty Clay Loam''' || 273 || 1 || 0.432
|-
| '''Sandy Clay Loam''' || 218.5 || 1.5 || 0.33
|-
| '''Silt Loam''' || 166.8 || 3.4 || 0.486
|-
| '''Loam''' || 88.9 || 7.6 || 0.434
|-
| '''Sandy Loam''' || 110.1 || 10.9 || 0.412
|-
| '''Loamy Sand''' || 61.3 || 29.9 || 0.401
|-
| '''Sand''' || 49.5 || 117.8 || 0.417
|}
 
Table 2 presents summary statistics for the Green-Ampt Parameters types. 
 
'''Table 2 USDA Summary Statistics for all Soil types for the Green-Ampt Infiltration Method''' 
{| class="wikitable "
| '''Stat''' || '''Suction (mm)''' || '''Hydraulic Conductivity (mm/hr)''' || '''Porosity (Fraction)'''
|-
| '''Min''' || 49.5 || 0.3 || 0.31
|-
| '''Max''' || 316.3 || 117.8 || 0.49
|-
| '''Mean''' || 184.04 || 15.86 || 0.4
|-
| '''SD''' || 94.82 || 34.92 || 0.05
|}
 
The comparison has been undertaken on a real-world model of the Plynlimon catchment in mid-Wales. The model was run with a real rainfall event from 2015 with a temporal resolution of 30 minutes as shown in the Figure 2. 
 

[[File:Fig2 GWY RF.png|600px|Figure 2: Plynlimon Rainfall]] 

'''Figure 2: Plynlimon Rainfall''' 
Within the Plynlimon catchment, the soil types were distributed within the catchment as shown in the map below although a single soil type has been selected for the comparison. Therefore, the effect seen is a consequence of changing a single soil parameter value. 
 

[[File:Fig3 GWY SoilMap.png|600px|Figure 3: Plynlimon Gwy Catchment Soil Types]] 

'''Figure 3: Plynlimon Gwy Catchment Soil Types''' 
 
For the purposes of this sensitivity analysis of the parameters a single soil type was used representing the general clay soil types that are present. 

== Green-Ampt Infiltration: User Parameters ==
Where the inbuilt USDA soil types, are not used, the user can specify their own values for the Suction, Hydraulic Conductivity, Porosity, Initial Soil Moisture and max ponding depth. What follows is a description of each parameter and the sensitivity to a 10% and 20% increase and decrease in the default values. 

== Capillary Suction Head ==
The suction head, represented in millimeters, is the capillary attraction on the soil voids. It is large for fine grain soils such as clays and smaller for sandy soils. 
To test the sensitivity of the simulated runoff at three gauged locations, as shown in figure 2, a low 49.5), mid representing the mean (184.4) and high (316.3) value of the suction head parameter was used with other parameters representing a clay soil (soil type 1). 
The larger the value the capillary suction head value, the more capillary action that is achieved and the amount of infiltration that takes place. This is shown by the increase in cumulative infiltration in the graph below with a greater cumulative infiltration for the 20% increase in the suction head. 

[[File:Fig4 sens to suction.png|600px|Figure 4: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Capillary Suction Head parameter in the Green-Ampt infiltration model.]] 

'''Figure 4: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Capillary Suction Head parameter in the Green-Ampt infiltration model.''' 
 
As a consequence of this, there is a less runoff generated as shown in Figure 5. The model is not particularly sensitive to the suction head parameter and this fits with observations made within the literature. 
 

[[File:Fig5 suctionhead.png|600px|Figure 5: Sensitivity of simulated flow at the Cefn-Brwn gauge locations in the Plynlimon Gwy catchment to the Suction head parameter in the Green-Ampt infiltration model.]] 

'''Figure 5: Sensitivity of simulated flow at the Cefn-Brwn gauge locations in the Plynlimon Gwy catchment to the Suction head parameter in the Green-Ampt infiltration model.''' 
 
''add comment on rising lim'' 
 
== Saturated Hydraulic Conductivity ==
The saturated hydraulic conductivity, measured in mm per hour, represents the ease that water can travel through the soil whilst it is saturated. The saturated hydraulic conductivity is the equivalent of the limiting infiltration rate in the Horton infiltration model. The hydraulic conductivity is high for sandy soils but low for compact clays. Again, the sensitivity was conducted by varying the value for clays, which itself is relatively by low, with three scenarios, low (0.3), mid representing the mean (15.86) and high (117.8). The results shown in figure 6 show that the parameter is very
sensitive to the changes in the hydraulic conductivity with the mid and high values providing a lot of infiltration and no runoff at the downstream gauge. As expected, the higher the hydraulic conductivity, then the more infiltration that occurs and the less runoff that is seen at the gauge. 
 

[[File:Fig6 hydroconduct.png|600px|Figure 6: Sensitivity of simulated flow at 3 gauge locations in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model when using a clay soil type.]] 

'''Figure 6: Sensitivity of simulated flow at 3 gauge locations in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model when using a clay soil type.''' 
 

[[File:Fig7 hydroconduct.png|600px|Figure 7: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model.]] 

'''Figure 7: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model.''' 

== Porosity ==
The porosity value represents the volume of dry voids per volume of soil and provides the maximum moisture deficit that is available, the difference between the moisture content at saturation and at the start of the simulation. 
Sandy soils tend to have lower porosities than clay soils, but drain to lower moisture contents between rainfall events because water is not held as strongly in the soil pores. Therefore, values of porosity tend to be higher for sandy soils when compared to clay soils. 
As shown in figure 8, the higher the porosity value, then the less runoff occurs due to increased infiltration although the model is not particular sensitive to the porosity value. 
 

[[File:Fig7 porosity sens.png|600px|Figure 7: Sensitivity of simulated flow at 3 gauge locations in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.]] 

'''Figure 8: Sensitivity of simulated flow at 3 gauge locations in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.''' 
 

[[File:Fig8 porosity sens.png|600px|Figure 8: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.]] 

'''Figure 9: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.''' 
 
== In built USDA soil type ==
The model was also run with the default in-build USDA soil types. Figure 11 shows the outputs. As expected the higher the soil type, then typically the more the infiltration and the lower the produced runoff. Soils 8-11, which represent sandy soils to not show any runoff in this example as the rainfall is all infiltrated. 
 
''Image 10'' 
'''Figure Caption NEEDED'''
 
== Summary ==
The Green-Ampt infiltration model is one of the available infiltration models within TUFLOW. There is a wide range of literature on Green-Ampt applications and some suggested parameter values for particular soil types, albeit soil types from the US. The 3 main Green-Ampt parameters have been tested to show the sensitivity of model outputs to the values as well as the variation in initial moisture. The results show that the model is relatively insensitive to the porosity value and suction head parameter. However, the outputs do show variations in runoff volume due to variations in both hydraulic conductivity and initial soil moisture. As part of any calibration exercise it is suggested that these would be the parameters that are most focused on. The hydraulic conductivity appears to affect the runoff volume throughout the event whereas the initial soil moisture has a limited impact at the beginning of the event before soils become saturated and results converge.

File:Fig7 hydroconduct.png

2020-06-01T14:59:10Z

TuflowJoe:

File:Fig6 hydroconduct.png

2020-06-01T14:57:13Z

TuflowJoe:

Green-Ampt Infiltration Parameters

2020-06-01T14:45:25Z

TuflowJoe:

== '''PAGE UNDER CONSTRUCTION''' ==

== Introduction ==
There are a number of methods available within TUFLOW to infiltrate water on the 2D surface into the sub-surface. These are Green-Ampt, Horton and Initial Loss/Continuing Loss. The models are used to represent hydrological losses particularly when direct rainfall is applied directly to the 2D surface and runoff is generated. As such, the infiltration module used, and the parameters selected, are important calibration parameters which should be used to calibrate simulated flows to observed flows. 

== Green-Ampt Infiltration ==
The Green-Ampt approach varies the rate of infiltration over time based on the soil’s hydraulic conductivity, suction, porosity and initial moisture content. The method assumes that as water begins to infiltrate the soil, a line developed differentiating between the ‘dry’ soil with moisture content θ_i) and the ‘wet’ soil (with moisture content equal to the porosity of the soil η). As the infiltrated water continues to move through the soil profile in a vertical direction, the soil moisture changes instantly from the initial content to a saturated state. This concept is shown in Figure 1. 
 

[[File:Fig_1_GA_Model.png|300px|Figure 1 Green-Ampt Model Concept]] 

'''Figure 1 Green-Ampt Model Concept''' 
''Figure courtesy of University of Texas''

 
The basic form of the Green-Ampt equation is expressed as follows: 
[[File:Basic_ga_equation.png|200px]] 

 
Where: 
:''t'' is time 
:K is the saturated hydraulic conductivity 
:∆''θ'' is defined as the soil capacity (the difference between the saturated and initial moisture content) 
:''φ'' is the soil suction head 
:h0 is the depth of ponded water 
:F(t) is the cumulative infiltration calculated from: 
[[File:Accumulative_infil.png|350px]] 

 
United States Department of Agriculture (USDA) soil types have been hardwired into TUFLOW and are presented in Table 1 along with the soil parameters. Alternatively, it is possible to define a customised soil type by specifying user defined values within the tsoilf. 
 
'''Table 1 USDA Soil types for the Green-Ampt Infiltration Method''' 
{| class="wikitable "
| '''USDA Soil Type''' || '''Suction (mm)''' || '''Hydraulic Conductivity (mm/hr)''' || '''Porosity (Fraction)'''
|-
| '''Clay''' || 316.3 || 0.3 || 0.385
|-
| '''Silty Clay''' || 292.2 || 0.5 || 0.423
|-
| '''Sandy Clay''' || 239 || 0.6 || 0.321
|-
| '''Clay Loam''' || 208.8 || 1 || 0.309
|-
| '''Silty Clay Loam''' || 273 || 1 || 0.432
|-
| '''Sandy Clay Loam''' || 218.5 || 1.5 || 0.33
|-
| '''Silt Loam''' || 166.8 || 3.4 || 0.486
|-
| '''Loam''' || 88.9 || 7.6 || 0.434
|-
| '''Sandy Loam''' || 110.1 || 10.9 || 0.412
|-
| '''Loamy Sand''' || 61.3 || 29.9 || 0.401
|-
| '''Sand''' || 49.5 || 117.8 || 0.417
|}
 
Table 2 presents summary statistics for the Green-Ampt Parameters types. 
 
'''Table 2 USDA Summary Statistics for all Soil types for the Green-Ampt Infiltration Method''' 
{| class="wikitable "
| '''Stat''' || '''Suction (mm)''' || '''Hydraulic Conductivity (mm/hr)''' || '''Porosity (Fraction)'''
|-
| '''Min''' || 49.5 || 0.3 || 0.31
|-
| '''Max''' || 316.3 || 117.8 || 0.49
|-
| '''Mean''' || 184.04 || 15.86 || 0.4
|-
| '''SD''' || 94.82 || 34.92 || 0.05
|}
 
The comparison has been undertaken on a real-world model of the Plynlimon catchment in mid-Wales. The model was run with a real rainfall event from 2015 with a temporal resolution of 30 minutes as shown in the Figure 2. 
 

[[File:Fig2 GWY RF.png|600px|Figure 2: Plynlimon Rainfall]] 

'''Figure 2: Plynlimon Rainfall''' 
Within the Plynlimon catchment, the soil types were distributed within the catchment as shown in the map below although a single soil type has been selected for the comparison. Therefore, the effect seen is a consequence of changing a single soil parameter value. 
 

[[File:Fig3 GWY SoilMap.png|600px|Figure 3: Plynlimon Gwy Catchment Soil Types]] 

'''Figure 3: Plynlimon Gwy Catchment Soil Types''' 
 
For the purposes of this sensitivity analysis of the parameters a single soil type was used representing the general clay soil types that are present. 

== Green-Ampt Infiltration: User Parameters ==
Where the inbuilt USDA soil types, are not used, the user can specify their own values for the Suction, Hydraulic Conductivity, Porosity, Initial Soil Moisture and max ponding depth. What follows is a description of each parameter and the sensitivity to a 10% and 20% increase and decrease in the default values. 

== Capillary Suction Head ==
The suction head, represented in millimeters, is the capillary attraction on the soil voids. It is large for fine grain soils such as clays and smaller for sandy soils. 
To test the sensitivity of the simulated runoff at three gauged locations, as shown in figure 2, a low 49.5), mid representing the mean (184.4) and high (316.3) value of the suction head parameter was used with other parameters representing a clay soil (soil type 1). 
The larger the value the capillary suction head value, the more capillary action that is achieved and the amount of infiltration that takes place. This is shown by the increase in cumulative infiltration in the graph below with a greater cumulative infiltration for the 20% increase in the suction head. 

[[File:Fig4 sens to suction.png|600px|Figure 4: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Capillary Suction Head parameter in the Green-Ampt infiltration model.]] 

'''Figure 4: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Capillary Suction Head parameter in the Green-Ampt infiltration model.''' 
 
As a consequence of this, there is a less runoff generated as shown in Figure 5. The model is not particularly sensitive to the suction head parameter and this fits with observations made within the literature. 
 

[[File:Fig5 suctionhead.png|600px|Figure 5: Sensitivity of simulated flow at the Cefn-Brwn gauge locations in the Plynlimon Gwy catchment to the Suction head parameter in the Green-Ampt infiltration model.]] 

'''Figure 5: Sensitivity of simulated flow at the Cefn-Brwn gauge locations in the Plynlimon Gwy catchment to the Suction head parameter in the Green-Ampt infiltration model.''' 
 
''add comment on rising lim'' 
 
== Saturated Hydraulic Conductivity ==
The saturated hydraulic conductivity, measured in mm per hour, represents the ease that water can travel through the soil whilst it is saturated. The saturated hydraulic conductivity is the equivalent of the limiting infiltration rate in the Horton infiltration model. The hydraulic conductivity is high for sandy soils but low for compact clays. Again, the sensitivity was conducted by varying the value for clays, which itself is relatively by low, with three scenarios, low (0.3), mid representing the mean (15.86) and high (117.8). The results shown in figure 6 show that the parameter is very
sensitive to the changes in the hydraulic conductivity with the mid and high values providing a lot of infiltration and no runoff at the downstream gauge. As expected, the higher the hydraulic conductivity, then the more infiltration that occurs and the less runoff that is seen at the gauge. 
 
''Image 6'' 
'''Figure 6: Sensitivity of simulated flow at 3 gauge locations in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model when using a clay soil type.''' 
 
== Porosity ==
The porosity value represents the volume of dry voids per volume of soil and provides the maximum moisture deficit that is available, the difference between the moisture content at saturation and at the start of the simulation. 
Sandy soils tend to have lower porosities than clay soils, but drain to lower moisture contents between rainfall events because water is not held as strongly in the soil pores. Therefore, values of porosity tend to be higher for sandy soils when compared to clay soils. 
As shown in figure 8, the higher the porosity value, then the less runoff occurs due to increased infiltration although the model is not particular sensitive to the porosity value. 
 

[[File:Fig7 porosity sens.png|600px|Figure 7: Sensitivity of simulated flow at 3 gauge locations in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.]] 

'''Figure 7: Sensitivity of simulated flow at 3 gauge locations in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.''' 
 

[[File:Fig8 porosity sens.png|600px|Figure 8: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.]] 

'''Figure 8: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.''' 
 
== Initial Soil Moisture Deficit ==
The initial soil moisture deficit determines how much infiltration capacity is available during the simulation. The moisture deficit is the difference between the saturated moisture content and the initial moisture content and is expressed as a fraction. 4 scenarios were simulated which set the initial soil moisture deficit at 0% (default), 25%, 50% and 75%. As expected, the wetter the soil at the beginning of the simulation, the less infiltration that occurs and therefore the increased runoff as shown by increased flows at the 3 flow gauges. The sensitivity of the simulated outputs to the initial soil moisture deficit appears to be more significant at the beginning of the event and becomes less sensitive as the first peak is experienced. This is due to the wetting of the soils which leads to saturation and the convergence in the potential infiltration rate. 
 
''Image 9/10'' 
'''Figure 10: Sensitivity of simulated flow at 3 gauge locations in the Plynlimon Gwy catchment to the initial soil moisture in the Green-Ampt infiltration model.''' 
 
== In built USDA soil type ==
The model was also run with the default in-build USDA soil types. Figure 11 shows the outputs. As expected the higher the soil type, then typically the more the infiltration and the lower the produced runoff. Soils 8-11, which represent sandy soils to not show any runoff in this example as the rainfall is all infiltrated. 
 
''Image 10/11'' 
'''Figure Caption NEEDED'''
 
== Summary ==
The Green-Ampt infiltration model is one of the available infiltration models within TUFLOW. There is a wide range of literature on Green-Ampt applications and some suggested parameter values for particular soil types, albeit soil types from the US. The 3 main Green-Ampt parameters have been tested to show the sensitivity of model outputs to the values as well as the variation in initial moisture. The results show that the model is relatively insensitive to the porosity value and suction head parameter. However, the outputs do show variations in runoff volume due to variations in both hydraulic conductivity and initial soil moisture. As part of any calibration exercise it is suggested that these would be the parameters that are most focused on. The hydraulic conductivity appears to affect the runoff volume throughout the event whereas the initial soil moisture has a limited impact at the beginning of the event before soils become saturated and results converge.

Green-Ampt Infiltration Parameters

2020-06-01T14:40:34Z

TuflowJoe:

== '''PAGE UNDER CONSTRUCTION''' ==

== Introduction ==
There are a number of methods available within TUFLOW to infiltrate water on the 2D surface into the sub-surface. These are Green-Ampt, Horton and Initial Loss/Continuing Loss. The models are used to represent hydrological losses particularly when direct rainfall is applied directly to the 2D surface and runoff is generated. As such, the infiltration module used, and the parameters selected, are important calibration parameters which should be used to calibrate simulated flows to observed flows. 

== Green-Ampt Infiltration ==
The Green-Ampt approach varies the rate of infiltration over time based on the soil’s hydraulic conductivity, suction, porosity and initial moisture content. The method assumes that as water begins to infiltrate the soil, a line developed differentiating between the ‘dry’ soil with moisture content θ_i) and the ‘wet’ soil (with moisture content equal to the porosity of the soil η). As the infiltrated water continues to move through the soil profile in a vertical direction, the soil moisture changes instantly from the initial content to a saturated state. This concept is shown in Figure 1. 
 

[[File:Fig_1_GA_Model.png|300px|Figure 1 Green-Ampt Model Concept]] 

'''Figure 1 Green-Ampt Model Concept''' 

The basic form of the Green-Ampt equation is expressed as follows: 
[[File:Basic_ga_equation.png|200px]] 

 
Where: 
:''t'' is time 
:K is the saturated hydraulic conductivity 
:∆''θ'' is defined as the soil capacity (the difference between the saturated and initial moisture content) 
:''φ'' is the soil suction head 
:h0 is the depth of ponded water 
:F(t) is the cumulative infiltration calculated from: 
[[File:Accumulative_infil.png|350px]] 

 
United States Department of Agriculture (USDA) soil types have been hardwired into TUFLOW and are presented in Table 1 along with the soil parameters. Alternatively, it is possible to define a customised soil type by specifying user defined values within the tsoilf. 
 
'''Table 1 USDA Soil types for the Green-Ampt Infiltration Method''' 
{| class="wikitable "
| '''USDA Soil Type''' || '''Suction (mm)''' || '''Hydraulic Conductivity (mm/hr)''' || '''Porosity (Fraction)'''
|-
| '''Clay''' || 316.3 || 0.3 || 0.385
|-
| '''Silty Clay''' || 292.2 || 0.5 || 0.423
|-
| '''Sandy Clay''' || 239 || 0.6 || 0.321
|-
| '''Clay Loam''' || 208.8 || 1 || 0.309
|-
| '''Silty Clay Loam''' || 273 || 1 || 0.432
|-
| '''Sandy Clay Loam''' || 218.5 || 1.5 || 0.33
|-
| '''Silt Loam''' || 166.8 || 3.4 || 0.486
|-
| '''Loam''' || 88.9 || 7.6 || 0.434
|-
| '''Sandy Loam''' || 110.1 || 10.9 || 0.412
|-
| '''Loamy Sand''' || 61.3 || 29.9 || 0.401
|-
| '''Sand''' || 49.5 || 117.8 || 0.417
|}
 
Table 2 presents summary statistics for the Green-Ampt Parameters types. 
 
'''Table 2 USDA Summary Statistics for all Soil types for the Green-Ampt Infiltration Method''' 
{| class="wikitable "
| '''Stat''' || '''Suction (mm)''' || '''Hydraulic Conductivity (mm/hr)''' || '''Porosity (Fraction)'''
|-
| '''Min''' || 49.5 || 0.3 || 0.31
|-
| '''Max''' || 316.3 || 117.8 || 0.49
|-
| '''Mean''' || 184.04 || 15.86 || 0.4
|-
| '''SD''' || 94.82 || 34.92 || 0.05
|}
 
The comparison has been undertaken on a real-world model of the Plynlimon catchment in mid-Wales. The model was run with a real rainfall event from 2015 with a temporal resolution of 30 minutes as shown in the Figure 2. 
 

[[File:Fig2 GWY RF.png|600px|Figure 2: Plynlimon Rainfall]] 

'''Figure 2: Plynlimon Rainfall''' 
Within the Plynlimon catchment, the soil types were distributed within the catchment as shown in the map below although a single soil type has been selected for the comparison. Therefore, the effect seen is a consequence of changing a single soil parameter value. 
 

[[File:Fig3 GWY SoilMap.png|600px|Figure 3: Plynlimon Gwy Catchment Soil Types]] 

'''Figure 3: Plynlimon Gwy Catchment Soil Types''' 
 
For the purposes of this sensitivity analysis of the parameters a single soil type was used representing the general clay soil types that are present. 

== Green-Ampt Infiltration: User Parameters ==
Where the inbuilt USDA soil types, are not used, the user can specify their own values for the Suction, Hydraulic Conductivity, Porosity, Initial Soil Moisture and max ponding depth. What follows is a description of each parameter and the sensitivity to a 10% and 20% increase and decrease in the default values. 

== Capillary Suction Head ==
The suction head, represented in millimeters, is the capillary attraction on the soil voids. It is large for fine grain soils such as clays and smaller for sandy soils. 
To test the sensitivity of the simulated runoff at three gauged locations, as shown in figure 2, a low 49.5), mid representing the mean (184.4) and high (316.3) value of the suction head parameter was used with other parameters representing a clay soil (soil type 1). 
The larger the value the capillary suction head value, the more capillary action that is achieved and the amount of infiltration that takes place. This is shown by the increase in cumulative infiltration in the graph below with a greater cumulative infiltration for the 20% increase in the suction head. 

[[File:Fig4 sens to suction.png|600px|Figure 4: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Capillary Suction Head parameter in the Green-Ampt infiltration model.]] 

'''Figure 4: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Capillary Suction Head parameter in the Green-Ampt infiltration model.''' 
 
As a consequence of this, there is a less runoff generated as shown in Figure 5. The model is not particularly sensitive to the suction head parameter and this fits with observations made within the literature. 
 

[[File:Fig5 suctionhead.png|600px|Figure 5: Sensitivity of simulated flow at the Cefn-Brwn gauge locations in the Plynlimon Gwy catchment to the Suction head parameter in the Green-Ampt infiltration model.]] 

'''Figure 5: Sensitivity of simulated flow at the Cefn-Brwn gauge locations in the Plynlimon Gwy catchment to the Suction head parameter in the Green-Ampt infiltration model.''' 
 
''add comment on rising lim'' 
 
== Saturated Hydraulic Conductivity ==
The saturated hydraulic conductivity, measured in mm per hour, represents the ease that water can travel through the soil whilst it is saturated. The saturated hydraulic conductivity is the equivalent of the limiting infiltration rate in the Horton infiltration model. The hydraulic conductivity is high for sandy soils but low for compact clays. Again, the sensitivity was conducted by varying the value for clays, which itself is relatively by low, with three scenarios, low (0.3), mid representing the mean (15.86) and high (117.8). The results shown in figure 6 show that the parameter is very
sensitive to the changes in the hydraulic conductivity with the mid and high values providing a lot of infiltration and no runoff at the downstream gauge. As expected, the higher the hydraulic conductivity, then the more infiltration that occurs and the less runoff that is seen at the gauge. 
 
''Image 6'' 
'''Figure 6: Sensitivity of simulated flow at 3 gauge locations in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model when using a clay soil type.''' 
 
== Porosity ==
The porosity value represents the volume of dry voids per volume of soil and provides the maximum moisture deficit that is available, the difference between the moisture content at saturation and at the start of the simulation. 
Sandy soils tend to have lower porosities than clay soils, but drain to lower moisture contents between rainfall events because water is not held as strongly in the soil pores. Therefore, values of porosity tend to be higher for sandy soils when compared to clay soils. 
As shown in figure 8, the higher the porosity value, then the less runoff occurs due to increased infiltration although the model is not particular sensitive to the porosity value. 
 

[[File:Fig7 porosity sens.png|600px|Figure 7: Sensitivity of simulated flow at 3 gauge locations in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.]] 

'''Figure 7: Sensitivity of simulated flow at 3 gauge locations in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.''' 
 

[[File:Fig8 porosity sens.png|600px|Figure 8: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.]] 

'''Figure 8: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.''' 
 
== Initial Soil Moisture Deficit ==
The initial soil moisture deficit determines how much infiltration capacity is available during the simulation. The moisture deficit is the difference between the saturated moisture content and the initial moisture content and is expressed as a fraction. 4 scenarios were simulated which set the initial soil moisture deficit at 0% (default), 25%, 50% and 75%. As expected, the wetter the soil at the beginning of the simulation, the less infiltration that occurs and therefore the increased runoff as shown by increased flows at the 3 flow gauges. The sensitivity of the simulated outputs to the initial soil moisture deficit appears to be more significant at the beginning of the event and becomes less sensitive as the first peak is experienced. This is due to the wetting of the soils which leads to saturation and the convergence in the potential infiltration rate. 
 
''Image 9/10'' 
'''Figure 10: Sensitivity of simulated flow at 3 gauge locations in the Plynlimon Gwy catchment to the initial soil moisture in the Green-Ampt infiltration model.''' 
 
== In built USDA soil type ==
The model was also run with the default in-build USDA soil types. Figure 11 shows the outputs. As expected the higher the soil type, then typically the more the infiltration and the lower the produced runoff. Soils 8-11, which represent sandy soils to not show any runoff in this example as the rainfall is all infiltrated. 
 
''Image 10/11'' 
'''Figure Caption NEEDED'''
 
== Summary ==
The Green-Ampt infiltration model is one of the available infiltration models within TUFLOW. There is a wide range of literature on Green-Ampt applications and some suggested parameter values for particular soil types, albeit soil types from the US. The 3 main Green-Ampt parameters have been tested to show the sensitivity of model outputs to the values as well as the variation in initial moisture. The results show that the model is relatively insensitive to the porosity value and suction head parameter. However, the outputs do show variations in runoff volume due to variations in both hydraulic conductivity and initial soil moisture. As part of any calibration exercise it is suggested that these would be the parameters that are most focused on. The hydraulic conductivity appears to affect the runoff volume throughout the event whereas the initial soil moisture has a limited impact at the beginning of the event before soils become saturated and results converge.

File:Fig8 porosity sens.png

2020-06-01T14:31:15Z

TuflowJoe:

File:Fig7 porosity sens.png

2020-06-01T14:27:52Z

TuflowJoe:

File:Fig4 sens to suction.png

2020-06-01T14:15:38Z

TuflowJoe:

File:Fig2 GWY RF.png

2020-06-01T13:48:07Z

TuflowJoe:

Green-Ampt Infiltration Parameters

2020-06-01T13:29:50Z

TuflowJoe:

== '''PAGE UNDER CONSTRUCTION''' ==

== Introduction ==
There are a number of methods available within TUFLOW to infiltrate water on the 2D surface into the sub-surface. These are Green-Ampt, Horton and Initial Loss/Continuing Loss. The models are used to represent hydrological losses particularly when direct rainfall is applied directly to the 2D surface and runoff is generated. As such, the infiltration module used, and the parameters selected, are important calibration parameters which should be used to calibrate simulated flows to observed flows. 

== Green-Ampt Infiltration ==
The Green-Ampt approach varies the rate of infiltration over time based on the soil’s hydraulic conductivity, suction, porosity and initial moisture content. The method assumes that as water begins to infiltrate the soil, a line developed differentiating between the ‘dry’ soil with moisture content θ_i) and the ‘wet’ soil (with moisture content equal to the porosity of the soil η). As the infiltrated water continues to move through the soil profile in a vertical direction, the soil moisture changes instantly from the initial content to a saturated state. This concept is shown in Figure 1. 
 

[[File:Fig_1_GA_Model.png|300px|Figure 1 Green-Ampt Model Concept]] 

'''Figure 1 Green-Ampt Model Concept''' 

The basic form of the Green-Ampt equation is expressed as follows: 
[[File:Basic_ga_equation.png|200px]] 

 
Where: 
:''t'' is time 
:K is the saturated hydraulic conductivity 
:∆''θ'' is defined as the soil capacity (the difference between the saturated and initial moisture content) 
:''φ'' is the soil suction head 
:h0 is the depth of ponded water 
:F(t) is the cumulative infiltration calculated from: 
[[File:Accumulative_infil.png|350px]] 

 
United States Department of Agriculture (USDA) soil types have been hardwired into TUFLOW and are presented in Table 1 along with the soil parameters. Alternatively, it is possible to define a customised soil type by specifying user defined values within the tsoilf. 
 
'''Table 1 USDA Soil types for the Green-Ampt Infiltration Method''' 
{| class="wikitable "
| '''USDA Soil Type''' || '''Suction (mm)''' || '''Hydraulic Conductivity (mm/hr)''' || '''Porosity (Fraction)'''
|-
| '''Clay''' || 316.3 || 0.3 || 0.385
|-
| '''Silty Clay''' || 292.2 || 0.5 || 0.423
|-
| '''Sandy Clay''' || 239 || 0.6 || 0.321
|-
| '''Clay Loam''' || 208.8 || 1 || 0.309
|-
| '''Silty Clay Loam''' || 273 || 1 || 0.432
|-
| '''Sandy Clay Loam''' || 218.5 || 1.5 || 0.33
|-
| '''Silt Loam''' || 166.8 || 3.4 || 0.486
|-
| '''Loam''' || 88.9 || 7.6 || 0.434
|-
| '''Sandy Loam''' || 110.1 || 10.9 || 0.412
|-
| '''Loamy Sand''' || 61.3 || 29.9 || 0.401
|-
| '''Sand''' || 49.5 || 117.8 || 0.417
|}
 
Table 2 presents summary statistics for the Green-Ampt Parameters types. 
 
'''Table 2 USDA Summary Statistics for all Soil types for the Green-Ampt Infiltration Method''' 
{| class="wikitable "
| '''Stat''' || '''Suction (mm)''' || '''Hydraulic Conductivity (mm/hr)''' || '''Porosity (Fraction)'''
|-
| '''Min''' || 49.5 || 0.3 || 0.31
|-
| '''Max''' || 316.3 || 117.8 || 0.49
|-
| '''Mean''' || 184.04 || 15.86 || 0.4
|-
| '''SD''' || 94.82 || 34.92 || 0.05
|}
 
The comparison has been undertaken on a real-world model of the Plynlimon catchment in mid-Wales. The model was run with a real rainfall event from 2015 with a temporal resolution of 30 minutes as shown in the Figure 2. 
 
''Image 2'' 
'''Figure 2: Plynlimon Rainfall''' 
Within the Plynlimon catchment, the soil types were distributed within the catchment as shown in the map below although a single soil type has been selected for the comparison. Therefore, the effect seen is a consequence of changing a single soil parameter value. 
 
''image 3'' 
'''Figure 3: Plynlimon Gwy Catchment Soil Types''' 
 
For the purposes of this sensitivity analysis of the parameters a single soil type was used representing the general clay soil types that are present. 

== Green-Ampt Infiltration: User Parameters ==
Where the inbuilt USDA soil types, are not used, the user can specify their own values for the Suction, Hydraulic Conductivity, Porosity, Initial Soil Moisture and max ponding depth. What follows is a description of each parameter and the sensitivity to a 10% and 20% increase and decrease in the default values. 

== Capillary Suction Head ==
The suction head, represented in millimeters, is the capillary attraction on the soil voids. It is large for fine grain soils such as clays and smaller for sandy soils. 
To test the sensitivity of the simulated runoff at three gauged locations, as shown in figure 2, a low 49.5), mid representing the mean (184.4) and high (316.3) value of the suction head parameter was used with other parameters representing a clay soil (soil type 1). 
The larger the value the capillary suction head value, the more capillary action that is achieved and the amount of infiltration that takes place. This is shown by the increase in cumulative infiltration in the graph below with a greater cumulative infiltration for the 20% increase in the suction head. 

''Image 4'' 
'''Figure 4: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the Capillary Suction Head parameter in the Green-Ampt infiltration model.''' 
 
As a consequence of this, there is a less runoff generated as shown in Figure 5. The model is not particularly sensitive to the suction head parameter and this fits with observations made within the literature. 
 
''Image 5'' 
'''Figure 5: Sensitivity of simulated flow at the Cefn-Brwn gauge locations in the Plynlimon Gwy catchment to the Suction head parameter in the Green-Ampt infiltration model.''' 
 
''add comment on rising lim'' 
 
== Saturated Hydraulic Conductivity ==
The saturated hydraulic conductivity, measured in mm per hour, represents the ease that water can travel through the soil whilst it is saturated. The saturated hydraulic conductivity is the equivalent of the limiting infiltration rate in the Horton infiltration model. The hydraulic conductivity is high for sandy soils but low for compact clays. Again, the sensitivity was conducted by varying the value for clays, which itself is relatively by low, with three scenarios, low (0.3), mid representing the mean (15.86) and high (117.8). The results shown in figure 6 show that the parameter is very
sensitive to the changes in the hydraulic conductivity with the mid and high values providing a lot of infiltration and no runoff at the downstream gauge. As expected, the higher the hydraulic conductivity, then the more infiltration that occurs and the less runoff that is seen at the gauge. 
 
''Image 6'' 
'''Figure 6: Sensitivity of simulated flow at 3 gauge locations in the Plynlimon Gwy catchment to the Saturated Hydraulic Conductivity parameter in the Green-Ampt infiltration model when using a clay soil type.''' 
 
== Porosity ==
The porosity value represents the volume of dry voids per volume of soil and provides the maximum moisture deficit that is available, the difference between the moisture content at saturation and at the start of the simulation. 
Sandy soils tend to have lower porosities than clay soils, but drain to lower moisture contents between rainfall events because water is not held as strongly in the soil pores. Therefore, values of porosity tend to be higher for sandy soils when compared to clay soils. 
As shown in figure 8, the higher the porosity value, then the less runoff occurs due to increased infiltration although the model is not particular sensitive to the porosity value. 
 
''Image 7'' 
'''Figure 7: Sensitivity of simulated flow at 3 gauge locations in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.''' 
 
''Image 8'' 
'''Figure 8: Sensitivity of cumulative infiltration in the Plynlimon Gwy catchment to the porosity parameter in the Green-Ampt infiltration model.''' 
 
== Initial Soil Moisture Deficit ==
The initial soil moisture deficit determines how much infiltration capacity is available during the simulation. The moisture deficit is the difference between the saturated moisture content and the initial moisture content and is expressed as a fraction. 4 scenarios were simulated which set the initial soil moisture deficit at 0% (default), 25%, 50% and 75%. As expected, the wetter the soil at the beginning of the simulation, the less infiltration that occurs and therefore the increased runoff as shown by increased flows at the 3 flow gauges. The sensitivity of the simulated outputs to the initial soil moisture deficit appears to be more significant at the beginning of the event and becomes less sensitive as the first peak is experienced. This is due to the wetting of the soils which leads to saturation and the convergence in the potential infiltration rate. 
 
''Image 9/10'' 
'''Figure 10: Sensitivity of simulated flow at 3 gauge locations in the Plynlimon Gwy catchment to the initial soil moisture in the Green-Ampt infiltration model.''' 
 
== In built USDA soil type ==
The model was also run with the default in-build USDA soil types. Figure 11 shows the outputs. As expected the higher the soil type, then typically the more the infiltration and the lower the produced runoff. Soils 8-11, which represent sandy soils to not show any runoff in this example as the rainfall is all infiltrated. 
 
''Image 10/11'' 
'''Figure Caption NEEDED'''
 
== Summary ==
The Green-Ampt infiltration model is one of the available infiltration models within TUFLOW. There is a wide range of literature on Green-Ampt applications and some suggested parameter values for particular soil types, albeit soil types from the US. The 3 main Green-Ampt parameters have been tested to show the sensitivity of model outputs to the values as well as the variation in initial moisture. The results show that the model is relatively insensitive to the porosity value and suction head parameter. However, the outputs do show variations in runoff volume due to variations in both hydraulic conductivity and initial soil moisture. As part of any calibration exercise it is suggested that these would be the parameters that are most focused on. The hydraulic conductivity appears to affect the runoff volume throughout the event whereas the initial soil moisture has a limited impact at the beginning of the event before soils become saturated and results converge.