If you were going to build an electronic brain in 2022, it might look something like the Neocortex supercomputer at the Pittsburgh Supercomputing Center at Carnegie Mellon University. …
Historically, the largest organizations in the world – the Global 2000 plus the biggest national government and academic research institutions – have always had the most complex data processing needs. …
What is that famous maxim in computer science about something that doubles on a regular cadence? …
Over the next year, we should get a good sense of how the Cerebras CS-1 system performs for dual HPC and AI workloads between installations at Argonne and Lawrence Livermore labs, EPCC, and the Pittsburgh Supercomputing Center (PSC). …
We have been excited about the possibilities of adding tiers of memory to systems, particularly persistent memories that are less expensive than DRAM but offer similar-enough performance and functionality to be useful. …
Exascale supercomputing is just as important to Europe as it is to the United States and China, but each of these geopolitical regions on Earth has its own way of developing architectures, funding their development and production, and figuring out where the best HPC centers are to host such machines to maximize their effectiveness. …
Nvidia is not the only company that has created specialized compute units that are good at the matrix math and tensor processing that underpins AI training and that can be repurposed to run AI inference. …
There is increasing competition coming at Nvidia in the AI training and inference market, and at the same time, researchers at Google, Cerebras, and SambaNova are showing off the benefits of porting sections of traditional HPC simulation and modeling code to their matrix math engines, and Intel is probably not far behind with its Habana Gaudi chips. …
In an ideal platform cloud, you would not know or care what the underlying hardware was and how it was composed to run your HPC – and now AI – applications. …
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