Hardware Selection Advice: Difference between revisions

We often get asked about the optimum computing setup to run TUFLOW models. While every model is different and will interact differently with your hardware there is some general advice that we can offer. InNote thethat sectionsrecommendations belowfocus youspecifically willon findrunning moreTUFLOW. detailedIt adviceis onhighly GPUrecommend to consult your IT team to confirm that all components of the machine are fully capable of supporting your intended uses and CPUmeet butyour requirements for quality, speed, and generally:durability.<br>

* TUFLOW Classic: A single model run can only use the CPU and cannot be run across multiple CPU cores or GPU hardware. In general terms: The maximum model size is dependent on the available CPU RAM and the runtime is driven by the CPU speed, architecture and cache size.

* TUFLOW HPC - Run on GPU Hardware: A single model run uses the GPU(s) for computation. In general terms: The maximum model size is dependent on the available GPU and CPU RAM and the runtime is driven by the CUDA core speed, the number of CUDA cores available and the GPU architecture. GPU performance is complex and is not easily inferred from GPU clock speed and number of cores, it is also very dependent on the ‘generation’ or architecture of GPU. As TUFLOW HPC requires some data exchange between GPU and CPU, the motherboard bus speeds and CPU speeds also play a role but typically a much lesser role compared to the GPU CUDA compute.

* TUFLOW FV - Run on CPU Hardware: A single model run uses CPU and is parallelised to run across multiple cores. In general terms: The maximum model size is dependent on the available CPU RAM and the runtime is determined by the CPU speed, the number of cores available to be run in parallel, chip architecture and cache size.

* TUFLOW FV - Run on GPU Hardware: A single model run uses the GPU(s) for computation. In general terms: The maximum model size is dependent on the available GPU and CPU RAM and the runtime is driven by the CUDA core speed, the number of CUDA cores available and the GPU architecture. GPU performance is complex and is not easily inferred from GPU clock speed and number of cores, it is also very dependent on the ‘generation’ or architecture of GPU. As TUFLOW HPCFV requires some data exchange between GPU and CPU, the motherboard bus speeds and CPU speeds also play a role but typically a much lesser role compared to the GPU CUDA compute.<br>

On ourThe <u>[[Hardware_Benchmarking_-_Results#CPU_Results | Hardware Benchmarking]]</u> page you can compareshows recently run combinations of GPU, CPU and RAM. These can be compared with the system youintended are planning tofor purchase. WeThe recommendrecommendation thatis if building a computer that youto seek advise from an appropriate computer hardware vendor who can advise on the compatibility and optimisation of yourthe setup.<br>

* TUFLOW HPC on GPU Hardware can be run in either single or double precision. However, for the vast majority of flood applications single precision is sufficient. We typically run our models on single precision. If you are unsure we recommend running with both the single and double precision solvers and comparing your results.

The precision solver you require will determine the type of GPU card that is best suited for your compute. For any given generation/architecture of cards, the “gaming” cards such as the GTX GeForce and RTX provide excellent single precision performance – typically comparable to that of the “scientific” cards such as the Tesla series. If double precision is required then the scientific cards are substantially faster, but these are also significantly more expensive. The Quadro series cards sit in between for both double precision performance and cost. When checking the specifications of the card it should provide you with a breakdown of the single and double precision throughput in flops. Single precision compute is typically sufficient for TUFLOW HPC modelling.

TUFLOW HPC on GPU hardware still uses the CPU to compute and store data (in CPU RAM) during model initialisation and for all 1D calculations. While we are working on improving our CPU RAM usage, currently we tend to find that CPU RAM is often the limiter to the size of the model domain you can run, particularly if using running over multiple GPU cards. During initialisation and simulation a model will typically require 4-6 times the amount of CPU RAM relative to GPU RAM. As an example, a model that utilises 11GB of GPU RAM (typical memory for high-end gaming card, and corresponds to about a 50 million cell model) the CPU RAM required during initialisation will typically be in range 44GB to 66GB. A model that fully utilises two 11 GB GPUs (i.e. a 100 million cell model) may require as much as 128GB of CPU RAM during initialisation. Note that anything more than 256GB of CPU RAM will exceed the limitations of consumer chipsets that are available in 2025 and requires more expensive workstation hardware - additionally, users should consult a hardware expert to check limitations of specific hardware.

===CUDA Cores, GPU Clock speed, and FLOPs ===

* TUFLOW HPC-GPU does not support SLI for inter-GPU communications. It does (as of build 2020-01-AA) auto detect and utilise peer-to-peer access over NVLink or PCI bus on the motherboard. Note that not all GPUs support peer-to-peer access.

===CPU Cores ===

* TUFLOW HPC - Run on GPU Hardware: The parallel processing is being done on the GPU card. However, TUFLOW HPC-GPU still uses the CPU for model initialisation and for 1D calculations. If multiple GPU cards are used, TUFLOW will use the equivalent number of CPU threads for controlling the GPUs and migrating data. So for a machine dedicated to HPC-GPU modelling, the number of CPU cores should be higher than the number of installed GPUs.

* TUFLOW HPC - Run on CPU Hardware: HPC model can also be run on multiple CPU cores. For the comparison of simulation speed, please refer to [[Hardware_Benchmarking_Topic_HPC_on_CPU_vs_GPU | HPC on CPU vs GPU]].

* TUFLOW Classic: TUFLOW Classic simulation can only use one CPU core due to the implicit nature of the numerical solution, for more information see this TUFLOW forum post. More CPU cores will enable running more simulations at the same time most efficiently.

Solid state hard drives are preferred for temporary storage as they are faster to write to than traditional hard drives. Large data files can then be transferred to a more permanent location.<br>