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    New Penguin/AMD High-Performance Computing Cluster coming to LLNL

    Lawrence Livermore National Laboratory, in partnership with Penguin Computing, AMD, and Mellanox Technologies, will accept delivery of Corona, a new unclassified high performance computing (HPC) cluster that will provide unique capabilities for Lab researchers and industry partners to explore data science, machine learning and big data analytics.

    The system will be provided by Penguin Computing and will be composed of AMD EPYC processors and AMD Radeon Instinct GPU (graphics processing unit) accelerators connected via a Mellanox HDR 200 Gigabit InfiniBand network.

    The system lends itself to applying machine learning and data analysis techniques to challenging problems in HPC and big data and will be used to support the National Nuclear Security Administration’s (NNSA) Advanced Simulation and Computing (ASC) program. The system will be housed by Livermore Computing (LC) in an unclassified site adjacent to the High Performance Computing Innovation Center (HPCIC), dedicated to partnerships with American industry.

    Procured through the Commodity Technology Systems (CTS-1) contract, Corona is part of a collaboration between AMD, Penguin Computing, Mellanox and LLNL. Corona will help the NNSA assess future architectures, fill institutional and ASC needs to develop leadership in data science and machine learning capabilities at scale, provide access to HPCIC partners, and extend a continuous collaboration vehicle for AMD, Penguin, Mellanox and LLNL.

    “Corona will provide an excellent platform for our research into cognitive computing algorithms and developing predictive simulations for both inertial confinement fusion applications as well as molecular dynamics simulations targeting precision medicine for oncology,” said LLNL Informatics Group Leader and computer scientist Brian Van Essen. “The unique computational resources and interconnect will allow us to continue to develop leading edge algorithms for scalable distributed deep learning. As deep learning becomes an integral part of many applications at the Laboratory, computational resources like Corona are vital to our ability to develop the next generation of scientific applications.”

     

    ELE Times Research Deskhttps://www.eletimes.com
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