Now that deep learning at traditional supercomputing centers is becoming a more pervasive combination, the infrastructure challenges of making both AI and simulations run efficiently on the same hardware and software stacks are emerging. …
We all expected that the Summit supercomputer at Oak Ridge National Lab would be a major part of pushing deep learning forward in HPC given its balanced GPU and IBM Power9 profile (not to mention the on-site expertise to get those graphics engines doing cutting-edge work outside of traditional simulations). …
Technologies often start out in one place and then find themselves in another. …
It is certainly true that no technology company can grow if they are not able to do business in China. …
A few years ago the market was rife with deep learning chip startups aiming at AI training. …
We did not plan it, but today has become make-your-eyes-bleed-with-chip architecture-patent-applications day. …
While deep learning models might not be able to simulate large-scale physical phenomena in the same way purpose-built supercomputers and their application stacks do, there is more research emerging that shows how traditional HPC simulations can be augmented, if not replaced in some parts, by neural networks. …
For any public cloud to succeed, it has to offer best of breed technologies reasonably close to the cutting edge and supporting the wide variety of compute that the enterprises of the world would otherwise acquire and run on premises. …
If it isn’t obvious, we like hardware here at The Next Platform. …
By definition, HPC is always at the cutting edge of computing, driving innovations in processor, system, and software design that eventually find their way into more mainstream computing systems. …
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