{"id":3805,"date":"2024-11-20T14:52:19","date_gmt":"2024-11-20T14:52:19","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2024\/11\/20\/the-need-for-speed-nvidia-accelerates-majority-of-worlds-supercomputers-to-drive-advancements-in-science-and-technology\/"},"modified":"2024-11-20T14:52:19","modified_gmt":"2024-11-20T14:52:19","slug":"the-need-for-speed-nvidia-accelerates-majority-of-worlds-supercomputers-to-drive-advancements-in-science-and-technology","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2024\/11\/20\/the-need-for-speed-nvidia-accelerates-majority-of-worlds-supercomputers-to-drive-advancements-in-science-and-technology\/","title":{"rendered":"The Need for Speed: NVIDIA Accelerates Majority of World\u2019s Supercomputers to Drive Advancements in Science and Technology"},"content":{"rendered":"
\n\t\t<\/span> <\/span> <\/span><\/span><\/p>\n

Starting with the release of CUDA in 2006, NVIDIA has driven advancements in AI and accelerated computing \u2014 and the most recent TOP500 list<\/a> of the world\u2019s most powerful supercomputers highlights the culmination of the company\u2019s achievements in the field.<\/p>\n

This year, 384 systems on the TOP500 list are powered by NVIDIA technologies. Among the 53 new to the list, 87% \u2014 46 systems \u2014 are accelerated. Of those accelerated systems, 85% use NVIDIA Hopper GPUs<\/a>, driving advancements in areas like climate forecasting<\/a>, drug discovery<\/a> and quantum simulation<\/a>.<\/p>\n

Accelerated computing is much more than floating point operations per second (FLOPS). It requires full-stack, application-specific optimization. At SC24 this week, NVIDIA announced the release of cuPyNumeric<\/a>, an NVIDIA CUDA-X<\/a> library that enables over 5 million developers to seamlessly scale to powerful computing clusters without modifying their Python code.<\/p>\n

At the conference, NVIDIA also revealed significant updates to the NVIDIA CUDA-Q<\/a> development platform, which empowers quantum researchers to simulate quantum devices at a scale previously thought computationally impossible.<\/p>\n

A New Era of Scientific Discovery With Mixed Precision and AI<\/b><\/h2>\n

Mixed-precision floating-point operations and AI have become the tools of choice for researchers grappling with the complexities of modern science. They offer greater speed, efficiency and adaptability than traditional methods, without compromising accuracy.<\/p>\n

This shift isn\u2019t just theoretical \u2014 it\u2019s already happening. At SC24, two Gordon Bell finalist projects revealed how using AI and mixed precision helped advance genomics and protein design.<\/p>\n

In his paper titled \u201cUsing Mixed Precision for Genomics,\u201d David Keyes, a professor at King Abdullah University of Science and Technology, used 0.8 exaflops of mixed precision to explore relationships between genomes and their generalized genotypes, and then to the prevalence of diseases to which they are subject.<\/p>\n

Similarly, Arvind Ramanathan, a computational biologist from the Argonne National Laboratory, harnessed 3 exaflops of AI performance on the NVIDIA Grace Hopper-powered Alps system to speed up protein design.<\/p>\n

To further advance AI-driven drug discovery and the development of lifesaving therapies, researchers can use NVIDIA BioNeMo<\/a>, powerful tools designed specifically for pharmaceutical applications. Now in open source, the BioNeMo Framework<\/a> can accelerate AI model creation, customization and deployment for drug discovery and molecular design.<\/p>\n

Across the TOP500, the widespread use of AI and mixed-precision floating-point operations reflects a global shift in computing priorities. A total of 249 exaflops of AI performance are now available to TOP500 systems, supercharging innovations and discoveries across industries.<\/p>\n

\"\"<\/a>
TOP500 total AI, FP32 and FP64 FLOPs by year.<\/figcaption><\/figure>\n

NVIDIA-accelerated TOP500 systems excel across key metrics like AI and mix-precision system performance. With over 190 exaflops of AI performance and 17 exaflops of single-precision (FP32), NVIDIA\u2019s accelerated computing platform is the new engine of scientific computing. NVIDIA also delivers 4 exaflops of double-precision (FP64) performance for certain scientific calculations that still require it.<\/p>\n

Accelerated Computing Is Sustainable Computing<\/b><\/h2>\n

As the demand for computational capacity grows, so does the need for sustainability.<\/p>\n

In the Green500 list of the world\u2019s most energy-efficient supercomputers, systems with NVIDIA accelerated computing rank among eight of the top 10. The JEDI system at EuroHPC\/FZJ, for example, achieves a staggering 72.7 gigaflops per watt, setting a benchmark for what\u2019s possible when performance and sustainability align.<\/p>\n

For climate forecasting, NVIDIA announced at SC24 two new NVIDIA NIM microservices for NVIDIA Earth-2<\/a>, a digital twin platform for simulating and visualizing weather and climate conditions. The CorrDiff NIM and FourCastNet NIM microservices can accelerate climate change modeling and simulation results by up to 500x.<\/p>\n

In a world increasingly conscious of its environmental footprint, NVIDIA\u2019s innovations in accelerated computing balance high performance with energy efficiency to help realize a brighter, more sustainable future.<\/p>\n

Watch the replay of NVIDIA\u2019s <\/i>special address<\/i><\/a> at SC24 and learn more about the company\u2019s news in the <\/i>SC24 online press kit<\/i><\/a>.<\/i><\/p>\n

See<\/i> notice<\/i><\/a> regarding software product information.<\/i><\/p>\n<\/p><\/div>\n","protected":false},"excerpt":{"rendered":"

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