Chips & SupplyUnited States

Nvidia found $1B under the couch to help secure American scientific computing dominance

Credited to The Register · theregister.com2d ago

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Commitment comes as GPUzilla prepares to fortify Uncle Sam's arsenal with at least seven AI-optimized supers

Nvidia on Thursday scraped together some spare change to commit $1 billion — about 1/60th of its quarterly profits — to funding US scientific discovery over the next five years.

The commitment, announced during an event in Washington, DC, aims to support the research and development of AI — or is it " super intelligence " now? — in fields including quantum computing, healthcare, and energy security.

Nvidia's choice of technologies is not surprising as it has long aspired to fuse AI and high-performance computing. And while LLM training and inference pay Nvidia's bills, the GPU giant has continued to introduce new CUDA libraries specifically for AI-assisted quantum computing, healthcare, drug discovery, and physics simulation.

The initiative is part of the US government's broader Genesis Mission, announced late last year by the Trump administration, which aims to use AI to drive the scientific discoveries necessary to ensure the country's continued technological leadership.

The program marks Nvidia's return to US supercomputing in a big way. The Department of Energy's (DoE) last flagship supercomputers powered by Nvidia, Summitand Sierra, were commissioned all the way back in 2018. The A100-based Perlmutter system launched in 2021 is also notable, but fell far short of the older systems. And while Nvidia never stopped building supers for the US government, they tended to be significantly smaller than the massive AMD-based platforms like Frontier and El Capitan.

That changed last year when Nvidia revealed it would supply the US government with no fewer than seven new supercomputers across the Argonne, Los Alamos, and Lawrence Berkeley National Laboratories.

Argonne will house the 100,000 Blackwell-GPU-based Solstice system. Built in collaboration with Oracle, the machine is largely aimed at emerging AI workloads, which don't depend on ultra-high precision computation and instead can get by with 16, eight or even four bits of precision.

That puts Nvidia in a strong position to meet the US government's goals under the Genesis Mission. Over the past few generations of GPUs, Nvidia has traded much of its accelerators' double-precision FP64 grunt in favor of higher throughput at precisions used by more lucrative AI workloads. In the case of its upcoming Vera Rubin platform, Nvidia relies heavily on an FP64 emulation capability based on the Ozaki scheme we looked at in more detail earlier this year.

Los Alamos' upcoming Mission and Vision systems will be powered by these same chips and take advantage of Nvidia's Quantum-X800 InfiniBand.

FP64 emulation comes with compromises, but for workloads that are predominantly AI-optimized and only occasionally need higher precision, it can be worthwhile for the additional matrix FLOPS.

While Nvidia is playing a much bigger role in US scientific computing efforts, it's worth noting that the DoE's biggest super — at least for pure FP64 — will still be powered by AMD. Oak Ridge National Laboratory's Discovery system, which is expected to come online 2029 will be built by HPE's Cray division and feature The House of Zen's FP64-optimized MI430X GPUs and Venice CPUs.

Discovery is expected to offer peak theoretical performance somewhere between 3.3 and 8.5 exaFLOPS depending on whether or not Oak Ridge gets a facility power upgrade.

Meanwhile, China's LineShine leads the Top500 ranking of Earth's mightiest computers. That system made its debut this northern spring with 2.2 exaFLOPS measured out of a 2.7 exaFLOPS of theoretical performance. With Uncle Sam's nose bloodied by China's return to the ranking, Oak Ridge may not have to fight hard for the extra power. ®

Original · The Register

Useful

Commitment comes as GPUzilla prepares to fortify Uncle Sam's arsenal with at least seven AI-optimized supers

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US government, tech giants and Biohub commit $1.8B to AI biology initiative

Credited to SiliconANGLE · siliconangle.com · 3d ago

Photograph · SiliconANGLE

More than a half-dozen public and private sector organizations are teaming up to develop biology datasets for artificial intelligence models.

The consortium, which announced the collaboration today, is committing $1.8 billion to the project.

Studying how a cell responds to a new therapy usually requires scientists to set up a physical test environment. That can take years of work and millions of dollars. A faster, more cost-efficient approach would be to test therapies using simulated cells. A simulation doesn’t require expensive lab equipment and can theoretically be spun up in a few hours.

Ultra-detailed, highly reliable simulations that can substitute lab equipment aren’t yet available. The reason is that creating such virtual cells would require a new generation of biology-optimized AI models. Training such models would only be possible with bigger biology datasets than those presently available to scientists.

The research initiative announced today aims to produce the necessary datasets. The biggest contributor to the project is the U.S. Department of Energy, which will commit more than $500 million over five years.

Some of the funds are earmarked for microscopy equipment. The hardware will be used to collect cell measurements for AI training datasets.

The Energy Department will prioritize three imaging methods: neutron scattering, X-ray and cryo-electron microscopy. The first two methods identify the materials that make up a biological sample. Neutron scattering equipment spots light elements and their isotopes, while X-ray machines detect heavier elements. Cryo-electron microscopy, the third imaging method on the list, maps out the internal structure of cells.

The second biggest contributor to the project is Biohub, a research nonprofit backed by Mark Zuckerberg. It’s committing $400 million to the initiative. Three quarters of the funds will go toward in-house research endeavors, while the rest will support external projects.

Like the Energy Department, Biohub will use cryo-electron microscopy to collect cell data. The nonprofit plans to use a specific variant of the technology called cryo-ET. It works by freezing a biological sample and exposing it to an electron beam. When the electrons come into contact with the sample, they change their course and thereby provide insight into its internal structure.

Biohub’s work will also extend to other areas. It plans to collect data about not only the inner workings of cells but also how different cells interact with one another. According to the group, the plan is to create images that contain millions to billions of cells.

The U.S. National Institutes of Health, or NIH, is also participating. It will provide access to biology datasets developed “through more than $500 million in prior federal investment aligned to this initiative.” Biohub and NIH will team up to organize the data into a form that can be more easily processed by AI models.

Google DeepMind, fellow Alphabet Inc. subsidiary Isomorphic Labs and Meta Platforms Inc. are together contributing $300 million. Nvidia Corp., meanwhile, plans to support aspects of the project that will use accelerators and specialized software to run research workloads. The tech giants will be joined by a half-dozen nonprofit research groups including the Allen Institute.

“An accurate predictive model of biology could dramatically accelerate scientific discovery by enabling scientists to perform experiments digitally,” said Biohub head of science Alex Rives. “The insights that come from this could unlock a far greater understanding of disease and open up completely new paths for cures.”

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