Performance is currently roughly comparable to running on GPUs at the same cost point, but note that we aren't making use of the TPU's matrix multiplication core at all currently. That leaves a lot of performance on the table, e.g., for hybrid deep learning models!
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Also note that the simulation currently isn't differentiable -- but that would be straightforward to add with the adjoint method. (You would not want to use naive back-propagation, because you would quickly run out of memory.)
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More broadly: I think the new paradigm of deep learning + auto-diff + accelerators has the potential to transform scientific computing. JAX is a decent platform for this already, and we're looking forward to making it even better!
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By the way, if you're excited about working on AI+scientific computing at Google, please reach out! I am looking to hire a PhD student intern this summer. We also have some great programs for visiting faculty & postdocs, as well as the AI residency program.
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To give a little more context: a likely focus would be using deep learning inside numerical methods for solving large scale PDE problems, particularly for computational fluid dynamics.
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Couldn’t help myself from re-running my benchmarks on TPU: https://github.com/dionhaefner/pyhpc-benchmarks/blob/master/results/colab.md … (just using one chip though)
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I want to work on my own Computer, not use the cloud. Can your Python code work in a GUI? I want to feed MIDI instrument data to an app, and make it produce coloured graphics, synesthetically. Maybe put in a shell and use it in a DAW as a VST3.
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