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Titlebook: OpenMP: Advanced Task-Based, Device and Compiler Programming; 19th International W Simon McIntosh-Smith,Michael Klemm,Jannis Klinkenb Confe

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書目名稱OpenMP: Advanced Task-Based, Device and Compiler Programming
副標(biāo)題19th International W
編輯Simon McIntosh-Smith,Michael Klemm,Jannis Klinkenb
視頻videohttp://file.papertrans.cn/702/701924/701924.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: OpenMP: Advanced Task-Based, Device and Compiler Programming; 19th International W Simon McIntosh-Smith,Michael Klemm,Jannis Klinkenb Confe
描述This book constitutes the proceedings of the 19th International Workshop on OpenMP, IWOMP 2023, held in Bristol, UK, during September 13–15, 2023..The 15 full papers presented in this book were carefully reviewed and selected from 20 submissions. The papers are divided into the following topical sections: OpenMP and AI;?Tasking Extensions;?OpenMP Offload Experiences;?Beyond Explicit GPU Support; and?OpenMP Infrastructure and Evaluation..
出版日期Conference proceedings 2023
關(guān)鍵詞computer systems; compiler; distributed computer systems; embedded systems; Field Programmable Gate Arra
版次1
doihttps://doi.org/10.1007/978-3-031-40744-4
isbn_softcover978-3-031-40743-7
isbn_ebook978-3-031-40744-4Series 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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How to?Efficiently Parallelize Irregular DOACROSS Loops Using Fine Granularity and?OpenMP Tasks: The, this paper proposes new constructs (. and .) and speculative dependency-types (., ., and .). An initial evaluation using different implementations to parallelize the . hottest loop shows that it is possible to achieve speed-ups of up to . with respect to the . version using Speculative Task Execut
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Improving a?Multigrid Poisson Solver with?Peer-to-Peer Communication and?Task Dependencieslts are presented for two high-performance computing systems with NVIDIA and AMD accelerators. We use four NVIDIA Tesla GV100 general-purpose graphics processing units to achieve a parallel efficiency of 94 percent for a solver based on V-cycles with seven multigrid levels.
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