Not so long ago, there was a question whether exascale supercomputers would be built from a very large number of thin nodes containing only modest amounts of parallelism or a smaller number of fat nodes powered by specialized accelerators and powerful manycore processors. …
It’s par for the course for AI chip startups to focus on peak performance on outdated benchmarks to appeal to the hardware folks who might give their gear a go for deep learning training or inference. …
As the era of exascale supercomputing approaches, Europe is making a concerted effort to become a first-class HPC power, on par with that of the United States, China, and Japan. …
Even though the race to the exascale computing is based entirely on an arbitrary milestone, it does serve to focus the attention of the HPC community to think about advancing the state-of-the-art for supercomputer hardware. …
Quantum computing hardware tends to garner the lion’s share of the attention from the press, but it’s the software toolkits for these devices that will be key to moving this technology out of the research lab. …
Support for unified memory across CPUs and GPUs in accelerated computing systems is the final piece of a programming puzzle that we have been assembling for about ten years now. …
For exascale hardware to be useful, systems software is going to have to be stacked up and optimized to bend that hardware to the will of applications. …
I have been frequently asked when the OpenMP and OpenACC directive APIs for parallel programming will merge, or when will one of them (usually OpenMP) will replace the other. …
To effectively make use of the level of concurrency in forthcoming exascale systems – hundreds of thousands of compute elements with millions of threads – requires some new thinking, both by programmers and in development tools. …
Every important benchmark needs to start somewhere.
The first round of MLperf results are in and while they might not deliver on what we would have expected in terms of processor diversity and a complete view into scalability and performance, they do shed light on some developments that go beyond sheer hardware when it comes to deep learning training. …
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