Re:What exascale computing could mean for chemistry
by dekhn on Monday September 12, 2022 @12:35PM
(#32812809)
at this point it seems like the folks building supercomputers for DoE and the folks building TPUs for Google should be talking. Both sides have interesting technology, but the communities are too far apart. Modern ML training is basically very similar to supercomputing that I think by working together, DeepMind could make better use of an exascale supercomputer than most scientists running simulations. Some of the network innovations in TPUv4 could be used in supercomputers.
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Re:What exascale computing could mean for chemistry
by alpineidyll3 on Monday September 12, 2022 @09:32PM
(#32819730)
100% agree with this. Although mini-batch SGD is much more parallel than most problems.
the scientific codes which parallelize poorly often parallelize poorly because they are written in ancient languages with support for whatever supercomputer interconnect bolted on poorly, Whereas TPU's + JAX have beautiful functional abstractions for distributed tensor computations.
Just funding re-writes of all the basic math/physics stack into a language with a PORTABLE parallel functional design and perhaps a compilation layer would definitely get more basic science done than this thing.