Titlebook: Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI; 17th Smoky Mountains Jeffrey Nichols,Becky

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Bettina Petersohn,Rainer-Olaf Schultzervations that will operate for multiple decades. To maximize the utility of NEON data, we envision edge computing systems that gather, calibrate, aggregate, and ingest measurements in an integrated fashion. Edge systems will employ machine learning methods to cross-calibrate, gap-fill and provision

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Christoph Hönnige,Sascha Kneip,Astrid Lorenzs of observed traffic. Network providers need intelligent solutions that can help quickly identify and understand anomalous behaviors at the network edge, allowing reactions to unexpected traffic or attacks on facilities and their peerings. However, due to lack of labeled data in network traffic ana

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Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI17th Smoky Mountains

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摘要

Integrating Deep Learning in Domain Sciences at Exascalen MagmaDNN) through a deep integration with existing HPC libraries, such as MAGMA and its modular memory management, MPI, CuBLAS, CuDNN, MKL, and HIP. Advancements are also illustrated through the use of algorithmic enhancements in reduced- and mixed-precision, as well as asynchronous optimization m