使用机器学习的HPC数据感知压缩

Q3 Computer Science

Operating Systems Review (ACM) Pub Date : 2022-06-14 DOI:10.1145/3544497.3544508

Julius Plehn, A. Fuchs, Michael Kuhn, Jakob Lüttgau, T. Ludwig

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引用次数: 0

摘要

虽然压缩可以提供大量的存储和成本节约，但它在HPC应用程序中的使用通常只是次要的问题。这部分是由于现有方法的不灵活性，即必须在整个应用程序中使用单个压缩算法，但也因为缺少对单个应用程序上下文中算法行为的洞察。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Data-Aware Compression for HPC using Machine Learning

While compression can provide significant storage and cost savings, its use within HPC applications is often only of secondary concern. This is in part due to the inflexibility of existing approaches where a single compression algorithm has to be used throughout the whole application but also because insights into the behaviour of the algorithms within the context of individual applications are missing.

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来源期刊

Operating Systems Review (ACM) Computer Science-Computer Networks and Communications

CiteScore

2.80

自引率

0.00%

发文量

期刊介绍： Operating Systems Review (OSR) is a publication of the ACM Special Interest Group on Operating Systems (SIGOPS), whose scope of interest includes: computer operating systems and architecture for multiprogramming, multiprocessing, and time sharing; resource management; evaluation and simulation; reliability, integrity, and security of data; communications among computing processors; and computer system modeling and analysis.