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Predicting Job Power Consumption Based on RJMS Submission Data in HPC Systems by Théo Saillant; Jean-Christophe Weill; Mathilde Mougeot is a book available to read on EtoBox.

What is Predicting Job Power Consumption Based on RJMS Submission Data in HPC Systems about?

This book constitutes the refereed proceedings of the 35th International Conference on High Performance Computing, ISC High Performance 2020, held in Frankfurt/Main, Germany, in June 2020.* The 27 revised full papers presented were carefully reviewed and selected from 87 submissions. The papers cover a broad range of topics such as architectures, networks & infrastructure; artificial intelligence and machine learning; data, storage & visualization; emerging technologies; HPC algorithms; HPC applications; performance modeling & measurement; programming models & systems software. *The conference was held virtually due to the COVID-19 pandemic. Chapters "Scalable Hierarchical Aggregation and Reduction Protocol (SHARP) Streaming-Aggregation Hardware Design and Evaluation", "Solving Acoustic Boundary Integral Equations Using High Performance Tile Low-Rank LU Factorization", "Scaling Genomics Data Processing with Memory-Driven Computing to Accelerate Computational Biology", "Footprint-Aware Power Capping for Hybrid Memory Based Systems", and "Pattern-Aware Staging for Hybrid Memory Systems" are available open access under a Creative Commons Attribution 4.0 International License via link.

Author

Théo Saillant; Jean-Christophe Weill; Mathilde Mougeot

Publisher

Springer International Publishing : Imprint: Springer

Published

2020

Language

EN

ISBN

9783030507428

Subjects

Computer Science, Engineering, Mathematics