Selene is a supercomputer developed by Nvidia, capable of achieving 63.460 petaflops, ranking as the fifth fastest supercomputer in the world, when it entered the list.[1][2][3][4] Selene is based on the Nvidia DGX system consisting of AMD CPUs, Nvidia A100 GPUs, and Mellanox HDDR networking.[5][3] Selene is based on the Nvidia DGX Superpod, which is a high performance turnkey supercomputer solution provided by Nvidia using DGX hardware. DGX Superpod is a tightly integrated system that combines high performance DGX compute nodes with fast storage and high bandwidth networking. It aims to provide a turnkey solution to high-demand machine learning workloads.[6] Selene was built in three months and is the fastest industrial system in the US while being the second-most energy-efficient supercomputing system ever.[7]
Selene utilizing 1080 AMD Epyc CPUs and 4320 A100 GPUs is used to train BERT, the natural language processor,[8] in less than 16 seconds, which usually takes most smaller systems about 20 minutes to execute.[9] IEEE Spectrum reported that as per December 2021 among all the commercially available supercomputing systems Selene topped all the results of MLPerf benchmark,[10] which is the benchmark developed by the consortium of artificial intelligence developers from academia, research labs, and industry aiming to unbiasedly evaluate the training and inference performance for hardware, software, and services used for AI.[11]
Selene is deployed by the Argonne National Laboratory to research different ways to end the coronavirus. It has been used to tackle problems around the concepts of protein docking and quantum chemistry, which are vital to developing an understanding of the coronavirus and a potential cure for it.[12][13]
Nvidia used Selene to train its GauGAN2 AI model, which is used in Nvidia Canvas[14] software to create art using artificial intelligence, using 10 million landscape images for training.[15] GauGAN2 AI model uses segmentation mapping, inpainting, and text-to-image generation in a single model to create art.[16]
See also edit
References edit
- ^ "Top500: Fugaku Keeps Crown, Nvidia's Selene Climbs to #5". HPCwire. 2020-11-16. Retrieved 2021-06-17.
- ^ Kobie, Nicole. "NVIDIA and the battle for the future of AI chips". Wired. ISSN 1357-0978. Retrieved 2021-06-17.
- ^ a b Strategy, Moor Insights and. "NVIDIA Provides More Details On Selene Supercomputer". Forbes. Retrieved 2021-06-17.
- ^ Freund, Karl. "NVIDIA Continues To Evolve, From Chips To Software To AI Data Centers". Forbes. Retrieved 2021-06-17.
- ^ "Nvidia Nabs #7 Spot on Top500 with Selene, Launches A100 PCIe Cards". HPCwire. 2020-06-22. Retrieved 2021-09-28.
- ^ Jarred Walton (2022-03-22). "Nvidia Reveals Hopper H100 GPU With 80 Billion Transistors". Tom's Hardware. Retrieved 2022-04-05.
- ^ "How realistic is Graphcore's 2024 timeline for ultra-intelligent supercomputers". Analytics India Magazine. 2022-03-16. Retrieved 2022-04-14.
- ^ "AI Weekly: AI supercomputers and facial recognition to verify taxpayers' identities". VentureBeat. 2022-01-28. Retrieved 2022-02-07.
- ^ Dorrier, Jason (2022-01-26). "Meta Is Making a Monster AI Supercomputer for the Metaverse". Singularity Hub. Retrieved 2022-02-07.
- ^ "NVIDIA: MLPerf AI Benchmarks". NVIDIA. Retrieved 2021-12-13.
- ^ "Is AI Training Outstripping Moore's Law?". IEEE Spectrum. 2021-12-02. Retrieved 2021-12-13.
- ^ "Nvidia Built Its Selene Supercomputer for Coronavirus Research in Just Three Weeks (Silicon Angle)". Informatics Institute. 2020-08-17. Retrieved 2021-12-13.
- ^ "Nvidia built its Selene supercomputer for coronavirus research in just 3 weeks". SiliconANGLE. 2020-08-14. Retrieved 2021-12-13.
- ^ "NVIDIA Canvas : Harness The Power Of AI". NVIDIA. Retrieved 2022-01-10.
- ^ Katie Wickens (2022-01-06). "Nvidia's upgraded AI art tool turned my obscure squiggles into a masterpiece". PC Gamer. Retrieved 2022-01-10.
- ^ Applications (2022-01-05). "Nvidia Canvas uses GauGAN2 AI model to achieve 4x resolution boost". AI News. Retrieved 2022-01-10.
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