GPU-Accelerated Fluid Dynamics - Petr Kodl | Podcast

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Hear Petr Kodl from Siemens talk about GPU-enabled acceleration of CFD simulation.
Representation of the benefits of running Simcenter STAR-CCM+ on GPUs provides computational fluid dynamics (CFD) capabilities to go faster while modeling the complexity. One of the constant challenges for computational fluid dynamics (CFD) engineers is to have a good level of simulation throughput.
CPUs, ARM, GPUs: In times of an increasingly heterogeneous hardware landscape, there are many choices, and simulation engineers need to identify the hardware that best fits their current needs.
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Podcast Recorded: March, 4th 2024 - Subscriber Release Count: 31,484.

[@alialabed6224]

This is one of the best talks in this podcast series. Much appreciation

[@JousefM]

Thanks mate!

[@Ashwah1993]

Fantastic job, Jousef. Thanks for making this podcast.

[@JousefM]

Thanks my friend!

[@damjangnjidic]

Great interview!

[@JousefM]

Thanks 🙂

[@mouna5415]

interesting podcast !

[@jsierra88]

26:05 what paper is he talking about?

[@deepankersingh4368]

Hey am working on a project in which I have to generate 1 crore mesh approximately. Best way to increase simulation speed? Meshing is also taking lot of time.

[@86baho]

Very interesting interview and crucial questions were raised. I am working with OpenFOAM and experience a mixed feelings since it runs exclusively on CPU. As I understand GPU foremost is exclusively used for computer graphics calculations, and CFD engineers has spotted its usefulness just to do number crunching for linear algebra. CPU are too overcompilated and low parallelism, and designed primary to run a complex logic for e.g. OS, it can do number crunching but parallelisation and memory bandwith is the bottleneck. Maybe in the future it will be a new Unit (chip) that do specifically number crunching, as a result a third main chip in your computer, who knows... I am personally struggling with all of this because you need to know to much to do some decent code, most likely it will be a custom code without using any commercial software. It would be interesting to ask Petr Kodl why exactly StarCMM+ is used? why not OpenFOAM?

[@hyperopt_]

star ccm+ is the product of the company he works for (Siemens), so it is not surprising that they are not developing a gpu port for openfoam :D

[@carlosegea3064]

You should divide these podcasts into shorter videos based on the conversation topics. Would be easier to find topics we want to learn. Thanks for the content.

[@JousefM]

Not the main prio atm but I used to do it for past episodes 🙂

[@ProjectPhysX]

I can only imagine the development cost to port a bloated 7 million line legacy CPU code to GPU, especially when the original authors are long retired. Porting is always troublesome as you're biased toward the old implementation and tend to overlook critical code optimization techniques specific to the new hardware. Siemens will need many years to catch up with modern native (multi-)GPU codes, if they can ever achieve acceptable compute efficiency all.

[@Dong-on5jv]

Can image that it would be an enormous work. But still have to do the work to catch up. Hope we can have a frame suits for all kinds of hardware even the future ones, otherwise changing to GPU version means little.

[@JousefM]

Interesting take, Moritz!

[@andersr9545]

I’ve heard that some GPU implementations of FVM uses the cells rather than the faces as the entity to loop over to compute fluxes. To my understanding you will end up computing a lot of duplicate fluxes since each cell compute all its own fluxes, but this tradeoff is apparently worth it for some reasons (maybe that you don’t have to beware of race conditioons and so(?). Is this an example of how porting from legacy cpu code would lead to suboptimal gpu code?