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Titlebook: Job Scheduling Strategies for Parallel Processing; 26th Workshop, JSSPP Dalibor Klusáček,Julita Corbalán,Gonzalo P. Rodrig Conference proce

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书目名称Job Scheduling Strategies for Parallel Processing
副标题26th Workshop, JSSPP
编辑Dalibor Klusáček,Julita Corbalán,Gonzalo P. Rodrig
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Job Scheduling Strategies for Parallel Processing; 26th Workshop, JSSPP Dalibor Klusáček,Julita Corbalán,Gonzalo P. Rodrig Conference proce
描述This book constitutes the thoroughly refereed post-conference proceedings of the 26th International Workshop on Job Scheduling Strategies for Parallel Processing, JSSPP 2023, held in St. Petersburg, FL, USA, during May 19, 2023..The 8 full papers and one keynote paper included in this book were carefully reviewed and selected from 14 submissions. The volume contains two sections: keynote and technical papers..
出版日期Conference proceedings 2023
关键词artificial intelligence; cloud computing; communication channels (information theory); communication sy
版次1
doihttps://doi.org/10.1007/978-3-031-43943-8
isbn_softcover978-3-031-43942-1
isbn_ebook978-3-031-43943-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Jason Hall,Arjun Lathi,David K. Lowenthal,Tapasya Patki
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Stragglers in Distributed Matrix Multiplicationon reduces latency overhead by . compared to existing dynamic load-balancing solutions, where . is the number of processors. Our solution overtakes redundancy-based solutions in all parameters: arithmetic cost, bandwidth cost, latency cost, memory footprint, and the number of stragglers it can toler
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An Efficient Approach Based on Graph Neural Networks for Predicting Wait Time in Job Schedulersmployed in this study. Our experiments using real historical logs confirmed that the proposed deep learning model achieved 0.3–7.9% higher prediction accuracy compared to the boosted decision tree and multi-layer perceptron models. An extensive analysis of the proposed deep learning model was perfor
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