Editor’s Choice

• Chip Geopolitics: If China Invades, Make Taiwan ‘Unwantable’ by Destroying TSMC, Military Paper Suggests

  

  US military planners are taking notice of a suggestion by two military scholars calling for the destruction of semiconductor foundry company Taiwan Semiconductor Manufacturing Co. (TSMC), whose fabs produce advanced microprocessors used in HPC and AI, in the event China invades the island nation A news story in today’ edition of Data Center Times cites the Nikkei Asia news service and a paper in the U.S. Army War College’s scholarly journal, Parameters, discussing the possibility of Taiwan adopting “’a scorched earth policy’ and wipe out its own semiconductor foundries in the wake of any Chinese invasion as a deterrent, U.S. [READ MORE…]

• Exascale: Rumors Circulate HPC Community Regarding Frontier’s Status

  

  By now you may have expected a triumphant announcement from the U.S. Department of Energy that the Frontier supercomputer, slated to be installed by the end of 2021 as the first U.S. exascale-class system, has been stood up with all systems go. But as of now, DOE (whose Oak Ridge National Laboratory will house Frontier) is foregoing a “mission accomplished” announcement and instead has issued a somewhat formal statement about Frontier’s status. Left unaddressed are rumors circulating through the HPC community of difficulties encountered in the late stages of Frontier system integration and fine tuning. Here’s the official statement on [READ MORE…]

• How Machine Learning Is Revolutionizing HPC Simulations

  

  Physics-based simulations, that staple of traditional HPC, may be evolving toward an emerging, AI-based technique that could radically accelerate simulation runs while cutting costs. Called “surrogate machine learning models,” the topic was a focal point in a keynote on Tuesday at the International Conference on Parallel Processing by Argonne National Lab’s Rick Stevens. Stevens, ANL’s associate laboratory director for computing, environment and life sciences, said early work in “surrogates,” as the technique is called, shows tens of thousands of times (and more) speed-ups and could “potentially replace simulations.” Surrogates can be looked at as an end-around to two big problems [READ MORE…]

• Double-precision CPUs vs. Single-precision GPUs; HPL vs. HPL-AI HPC Benchmarks; Traditional vs. AI Supercomputers

  

  If you’ve wondered why GPUs are faster than CPUs, in part it’s because GPUs are asked to do less – or, to be more precise, to be less precise. Next question: So if GPUs are faster than CPUs, why aren’t GPUs  the mainstream, baseline processor used in HPC server clusters? Again, in part it gets back to precision. In many workload types, particularly traditional HPC workloads, GPUs aren’t precise enough. Final question: So if GPUs and AI are inextricably linked, particularly for training machine learning models, and if GPUs are less precise than CPUs, does that mean AI is imprecise? [READ MORE…]

• 6,000 GPUs: Perlmutter to Deliver 4 Exaflops, Top Spot in AI Supercomputing

  

  The U.S. National Energy Research Scientific Computing Center today unveiled the Perlmutter HPC system, a beast of a machine powered by 6,159 Nvidia A100 GPUs and delivering 4 exaflops of mixed precision performance. Perlmutter is based on the HPE Cray Shasta platform, including Slingshot interconnect, a heterogeneous system with both GPU-accelerated and CPU-only nodes. The system is being installed in two phases – today’s unveiling is Phase 1, which includes the system’s GPU-accelerated nodes and scratch file system. Phase 2 will add CPU-only nodes later in 2021. “That makes Perlmutter the fastest system on the planet on the 16- and 32-bit [READ MORE…]