{"id":2143,"date":"2022-05-30T17:43:19","date_gmt":"2022-05-30T17:43:19","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2022\/05\/30\/nvidia-accelerates-ai-digital-twins-quantum-computing-and-edge-hpc-at-isc-2022\/"},"modified":"2022-05-30T17:43:19","modified_gmt":"2022-05-30T17:43:19","slug":"nvidia-accelerates-ai-digital-twins-quantum-computing-and-edge-hpc-at-isc-2022","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2022\/05\/30\/nvidia-accelerates-ai-digital-twins-quantum-computing-and-edge-hpc-at-isc-2022\/","title":{"rendered":"NVIDIA Accelerates AI, Digital Twins, Quantum Computing and Edge HPC at ISC 2022"},"content":{"rendered":"
\n

Researchers grappling with today\u2019s grand challenges are getting traction with accelerated computing<\/a>, as showcased at ISC<\/a>, Europe\u2019s annual gathering of supercomputing experts.<\/p>\n

Some are building digital twins to simulate new energy sources. Some use AI+HPC to peer deep into the human brain.<\/p>\n

Others are taking HPC to the edge with highly sensitive instruments or accelerating simulations on hybrid quantum systems, said Ian Buck, vice president of accelerated computing at NVIDIA, at an ISC special address in Hamburg.<\/p>\n

Delivering 10 AI Exaflops<\/b><\/h2>\n

For example, a new supercomputer at Los Alamos National Laboratory (LANL) called Venado will deliver 10 exaflops of AI performance to advance work in areas such as materials science and renewable energy.<\/p>\n

LANL researchers target 30x speedups in their computational multi-physics applications with NVIDIA GPUs, CPUs and DPUs in the system, named after a peak in northern New Mexico.<\/p>\n

<\/p>\n

\"LANL's<\/p>\n

<\/a><\/p>\n

Venado will use NVIDIA Grace Hopper Superchips<\/a> to run workloads up to 3x faster than prior GPUs. It also packs NVIDIA Grace CPU Superchips<\/a> to provide twice the performance per watt of traditional CPUs on a long tail of unaccelerated applications.<\/p>\n

BlueField Gathers Momentum<\/b><\/h2>\n

The LANL system is among the latest of many<\/a> around the world to embrace NVIDIA BlueField DPUs<\/a> to offload and accelerate communications and storage tasks from host CPUs.<\/p>\n

Similarly, the Texas Advanced Computing Center is adding BlueField-2 DPUs to the NVIDIA Quantum InfiniBand<\/a> network on Lonestar6. It will become a development platform for cloud-native supercomputing<\/a>, hosting multiple users and applications with bare-metal performance while securely isolating workloads.<\/p>\n

\u201cThat\u2019s the architecture of choice for next-generation supercomputing and HPC clouds,\u201d said Buck.<\/p>\n

Exascale in Europe<\/b><\/h2>\n

In Europe, NVIDIA and SiPearl are collaborating to expand the ecosystem of developers building exascale computing on Arm. The work will help the region\u2019s users port applications to systems that use SiPearl\u2019s Rhea and future Arm-based CPUs together with NVIDIA accelerated computing and networking technologies.<\/p>\n

Japan\u2019s Center for Computational Sciences, at the University of Tsukuba, is pairing NVIDIA H100 Tensor Core GPUs<\/a> and x86 CPUs on an NVIDIA Quantum-2 InfiniBand platform. The new supercomputer will tackle jobs in climatology, astrophysics, big data, AI and more.<\/p>\n

The new system will join the 71% on the latest TOP500 list of supercomputers that have adopted NVIDIA technologies. In addition, 80% of new systems on the list also use NVIDIA GPUs, networks or both and NVIDIA\u2019s networking platform is the most popular interconnect for TOP500 systems.<\/p>\n

HPC users adopt NVIDIA technologies because they deliver the highest application performance for established supercomputing workloads \u2014 simulation, machine learning, real-time edge processing \u2014 as well as emerging workloads like quantum simulations and digital twins.<\/p>\n

Powering Up With Omniverse<\/b><\/h2>\n

Showing what these systems can do, Buck played a demo of a virtual fusion power plant<\/a> that researchers in the U.K. Atomic Energy Authority and the University of Manchester are building in NVIDIA Omniverse<\/a>. The digital twin aims to simulate in real time the entire power station, its robotic components \u2014 even the behavior of the fusion plasma at its core.<\/p>\n<\/p>\n

NVIDIA Omniverse<\/a>, a 3D design collaboration and world simulation platform, lets distant researchers on the project work together in real time while using different 3D applications. They aim to enhance their work with NVIDIA Modulus<\/a>, a framework for creating physics-informed AI models.<\/p>\n

\u201cIt\u2019s incredibly intricate work that\u2019s paving the way for tomorrow\u2019s clean renewable energy sources,\u201d said Buck.<\/p>\n

AI for Medical Imaging<\/b><\/h2>\n

Separately, Buck described how researchers created a library of 100,000 synthetic images of the human brain on NVIDIA Cambridge-1<\/a>, a supercomputer dedicated to advances in healthcare with AI.<\/p>\n

A team from King\u2019s College London used MONAI<\/a>, an AI framework for medical imaging, to generate lifelike images that can help researchers see how diseases like Parkinson\u2019s develop.<\/p>\n

\u201cThis is a great example of HPC+AI making a real contribution to the scientific and research community,\u201d said Buck.<\/p>\n

HPC at the Edge<\/b><\/h2>\n

Increasingly, HPC work extends beyond the supercomputer center. Observatories, satellites and new kinds of lab instruments need to stream and visualize data in real time.<\/p>\n

For example, work in lightsheet microscopy at Lawrence Berkeley National Lab is using NVIDIA Clara Holoscan<\/a> to see life in real time at nanometer scale, work that would require several days on CPUs.<\/p>\n

To help bring supercomputing to the edge, NVIDIA is developing Holoscan for HPC, a highly scalable version of our imaging software to accelerate any scientific discovery. It will run across accelerated platforms from Jetson AGX modules and appliances to quad A100 servers.<\/p>\n

\u201cWe can\u2019t wait to see what researchers will do with this software,\u201d said Buck.<\/p>\n

Speeding Quantum Simulations<\/b><\/h2>\n

In yet another vector of supercomputing, Buck reported on the rapid adoption of NVIDIA cuQuantum<\/a>, a software development kit to accelerate quantum circuit simulations on GPUs.<\/p>\n

Dozens of organizations<\/a> are already using it in research across many fields. It\u2019s integrated into major quantum software frameworks so users can access GPU acceleration without any additional coding.<\/p>\n

Most recently, AWS announced the availability of cuQuantum in its Braket service<\/a>. And it demonstrated how cuQuantum can provide up to a 900x speedup<\/a> on quantum machine learning workloads while reducing costs 3.5x.<\/p>\n

\u201cQuantum computing has tremendous potential, and simulating quantum computers on GPU supercomputers is essential to move us closer to valuable quantum computing\u201d said Buck. \u201cWe\u2019re really excited to be at the forefront of this work,\u201d he added.<\/p>\n

A video of the full address will be posted here Tuesday, March 31 at 9am PT.<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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