We have written much over the last few years about the convergence of deep learning and traditional supercomputing but as the two grow together, it is clear that the tools for one area don’t always mesh well with those of the other. …
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. …
Advances in visualization are essential for managing—and maximizing value from—the rising flood of data, the growing sophistication of simulation codes, the convergence of machine learning (ML) and simulation workloads, and the development of extreme-scale computers. …
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). …
As we argued a few weeks ago, the cloud is where quantum competition gets real. …
European supercomputing centers are known for deploying innovative architectures and working on cutting-edge applications, but the sheer number of these systems lags rather far behind the U.S. …
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. …
Intel is gearing up the FPGA user base it inherited from Altera for the release of Stratix 10 hardware and companion application acceleration stack. …
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