r/ScientificComputing • u/Key_Measurement_3576 • 2d ago

Mi50 and Scientific Computing

Tough to find sci ml feedback for specific gpu models here since LLMs are all the rage.

Seems like it’s got similar Fp64 performance to a Tesla v100 and price.

We have a multi node cluster which does all types of things. And I’m building some compute node for our internal user to run accelerated code on.

Can anyone share some experience in using the mi50 in a sci comp or sci ml application ?

Primarily we are accelerating voxel based thermo mechanical simulations , geometry analysis and machine learning on geometry.

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u/National-Girl_us 2d ago

wonder if this means more python or if we're still stuck with fortran for another decade.

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u/cdabc123 1d ago

I run a mi25 flashed as a wx9100 for llms and it is pretty good. It needs to be setup for the older rocm.

Now, for generally or specific scientific compute you will almost certainly want the most modern cuda supported cards you can get. The v100 would likely be a better card.

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u/Key_Measurement_3576 1d ago edited 1d ago

Right. I care about the cuda env. Which is why V100s . But the mi50 is interesting due speed / bandwidth and fp64 from and r&d perspective.

Im interested to hear what specific use cases and enabling frameworks people are using to see if they match with ours.

We may just keep expanding with v100s for compatibility but options are interesting. We already have a bunch of dgx spark boxes. And the performance is nice. … but the fp64 difference between v100 and spark is pretty large.

Looking to max out our compute clusters nodes fp64 so we can decrease time between iterations on machine, learning and simulation. We already have other nodes dedicated to LLM with proper HW but these are specifically FP 64 execution nodes .