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

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書(shū)目名稱Job Scheduling Strategies for Parallel Processing
副標(biāo)題26th Workshop, JSSPP
編輯Dalibor Klusá?ek,Julita Corbalán,Gonzalo P. Rodrig
視頻videohttp://file.papertrans.cn/501/500974/500974.mp4
叢書(shū)名稱Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Job Scheduling Strategies for Parallel Processing; 26th Workshop, JSSPP Dalibor Klusá?ek,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
關(guān)鍵詞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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