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How does this compare with one of Google's TPU v4 pods? https://www.hpcwire.com/2021/05/20/google-launches-tpu-v4-ai... ?
Perlmutter's 120 petaflops peak would place it very favorably in TOP500's rankings, within the top five – if these flops aren't apples-to-oranges with what those rankings measure. Can anyone shed more light on the distinctions involved?
Also, I wouldn't want their bills for chilled water or electricity. My puny 96-thread EPYC ranges from 300 - 850 W and heats my room. :B
> Python programmers will be able to use RAPIDS, Nvidia’s open software suite for GPU-enabled data science Ah not to be confused with OpenAI Rapid described at https://openai.com/blog/openai-five/ ^_^
Disclaimer disclaimer: Language religious wars are dumb, unless we're talking about Rust. I can't find too much bad to say about it other than it doesn't look like Pony. :D Let me go build a programming language with implicit lifetimes and no GC as something no one will ever use. Hang on... gimme a minute.
I'm curious what exactly distinguishes the "AI supercomputer" category from whatever the rest of the TOP500 are. Perlmutter's 120 petaflops peak would place it very favorably in TOP500's rankings, within the top five – if these flops aren't apples-to-oranges with what those rankings measure. Can anyone shed more light on the distinctions involved?
> Note: Perlmutter’s “AI performance” is based on Nvidia’s half-precision numerical format (FP16 Tensor Core) with Nvidia’s sparsity feature enabled.
FP16 is a 16 bit floating point format. FLOPS for top 500 are measured with LINPACK HPL, which says it is over 64 bit floating point values (I think):
> HPL is a software package that solves a (random) dense linear system in double precision (64 bits) arithmetic on distributed-memory computers. It can thus be regarded as a portable as well as freely available implementation of the High Performance Computing Linpack Benchmark.
(from https://www.netlib.org/benchmark/hpl/)
This isn't totally disingenuous though. These FP16 operations are very useful for some kinds of calculations.
GPFS, Lustre, Panasas (PanFS / pNFS), BeeGFS are just about the only games in town for HPC pFSes. OrangeFS maybe. Also, I wouldn't want their bills for chilled water or electricity. My puny 96-thread EPYC ranges from 300 - 850 W and heats my room. :B
Not just for perl! > namesake of Saul Perlmutter, an astrophysicist at Berkeley Lab who shared the 2011 Nobel Prize in Physics for his contributions to research showing that the expansion of the universe is accelerating
I'm curious what exactly distinguishes the "AI supercomputer" category from whatever the rest of the TOP500 are. Perlmutter's 120 petaflops peak would place it very favorably in TOP500's rankings, within the top five – if these flops aren't apples-to-oranges with what those rankings measure. Can anyone shed more light on the distinctions involved?
I believe there's a hint towards the end of the article: > Note: Perlmutter’s “AI performance” is based on Nvidia’s half-precision numerical format (FP16 Tensor Core) with Nvidia’s sparsity feature enabled. FP16 is a 16 bit floating point format. FLOPS for top 500 are measured with LINPACK HPL, which says it is over 64 bit floating point values (I think): > HPL is a software package that solves a (random) dense linea…
ML for example. If you use 16bit precision you can fit the model in half the memory and your lookups are twice as fast. Newer GPU models offer "mixed precision mode" It takes some doing to get that working in your tooling though.