Organisation Cloud Software Execution: Difference between revisions
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First set of questions answered, based on list of questions provided by Pavlina via email. |
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== Q13: Is there a developed service to run large numbers of model runs on the cloud, if we cannot set it up ourselves? ==
As of 2019, TUFLOW offer an [[TUFLOW Cloud Simulation Service|on-demand cloud simulation service]] that may suit your needs if your project is sufficiently urgent or large. As of 2023, you may find third parties providing services on the cloud as well, and TUFLOW may support use of its software in such services.
== Q14: Which machine size / hardware type do you recommend for my model runs? ==
Hardware selection is very specific to the modelling requirements of each organisation and project. There is no one-size-fits-all recommendation to make.
However, some comments that generally apply:
* As with physical hardware, top speed comes at a premium. If you compare model run times between different VM sizes, you may find that running on the slower machines may work out cheaper for a certain amount of work, than using the faster ones. Of course, you will have to consider project lead time, and time spent on licences as well.
* For most cloud providers, the number of vCPU cores scales together with the type and number of available GPUs. And together with vCPUs, the amount of available RAM and storage scales up as well. As a result, you may end up with a lot of unused resources on some machine types.
* If you're considering purchasing cloud infrastructure for permanent use, keep in mind that Virtual Desktop Infrastructure solutions often share resources like GPUs between many users. You may find that a specific type of hardware works really well in a test setup, where you're the only user on it, but performs really poorly when under load from many users. If you purchase access to a cloud VM directly, you will have it all to yourself, but additional infrastructure on top of the VMs may affect your performance greatly.
* Conversely, when selecting a VM type that provides access to only 'half' of a data centre VM, you don't have to worry about negative performance impact. This type of sharing (that your IT can also achieve in your own data centre with NVIDIA MIG) still ensures that you always get full access to a dedicated part of the GPU and performance should be as expected.
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