Academia, hyperscalers and scientific researchers have been big beneficiaries of high performance computing and AI infrastructure. Yet businesses have largely been on the outside looking in. NVIDIA DGX SuperPOD provides businesses a proven design formula for building and running enterprise-grade AI infrastructure with extreme scale. The reference architecture gives businesses a prescription to follow to avoid exhaustive, protracted design and deployment cycles and capital budget overruns. It's available as a consumable solution that now integrates with the leading names in data center IT -- including DDN, IBM, Mellanox and NetApp -- and is fulfilled through a network of qualified resellers.
You can't be first if you're not fast. Inside the world's top companies, teams of researchers and data scientists are creating ever more complex AI models, which need to be trained, fast. And that's why the AI training results released today by MLPerf matter. Across all six of six MLPerf categories, NVIDIA demonstrated world-class performance and versatility. Our AI platform set eight records in training performance, including three in overall performance at scale and five on a per-accelerator basis.
Nvidia is launching the $100 million Cambridge-1, the most powerful supercomputer in the United Kingdom, and it is making it available to external researchers in the U.K. health care industry. The machine will be used for AI research in health care, and it's one of the world's fastest supercomputers. Nvidia will make it available to accelerate research in digital biology, genomics, and quantum computing. Nvidia is collaborating with AstraZeneca, maker of one of the COVID-19 vaccines, to fuel faster drug discoveries and creating a transformer-based generative AI model for chemical structures. Transformer-based neural network architectures, which have become available only in the last several years, allow researchers to leverage massive datasets using self-supervised training methods, avoiding the need for manually labeled examples during pre-training.
Nvidia Corp. said today it managed to build Selene, the world's seventh-fastest supercomputer that's used by the Argonne National Laboratory to research ways to stop the coronavirus, in just under three weeks. The Selene supercomputer has been deployed to tackle problems around concepts such as protein docking and quantum chemistry, which are key to developing an understanding of the coronavirus and a potential cure for the COVID-19 disease. Nvidia said Selene is based on its most advanced DGX SuperPOD architecture, which is a new system developed for artificial intelligence workloads that was announced earlier this year. The DGX SuperPOD incorporates eight of Nvidia's latest A100 graphics processing units, which are designed for data analytics, scientific computing and cloud graphics workloads. Building the Selene supercomputer in such rapid time during the middle of a pandemic was no easy feat, but Nvidia said in a blog post it was able to draw on its earlier experience of piecing together supercomputers based on its older DGX-2 systems.
NetApp, a global, cloud-led, data-centric software company, announced that NetApp EF600 all-flash NVMe storage combined with the BeeGFS parallel file system is now certified for NVIDIA DGX SuperPOD. The new certification simplifies artificial intelligence (AI) and high-performance computing (HPC) infrastructure to enable faster implementation of these use cases. Since 2018, NetApp and NVIDIA have served hundreds of customers with a range of solutions, from building AI Centers of Excellence to solving massive-scale AI training challenges. The qualification of NetApp EF600 and BeeGFS file system for DGX SuperPOD is the latest addition to a complete set of AI solutions that have been developed by the companies. NetApp's portfolio of NVIDIA-accelerated solutions includes ONTAP AI to eliminate guesswork for faster adoption by using a field-proven reference architecture as well as a preconfigured, integrated solution that is easy to procure and deploy in a turnkey manner.