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Titlebook: OpenMP: Heterogenous Execution and Data Movements; 11th International W Christian Terboven,Bronis R. de Supinski,Matthias Conference proce

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書目名稱OpenMP: Heterogenous Execution and Data Movements
副標題11th International W
編輯Christian Terboven,Bronis R. de Supinski,Matthias
視頻videohttp://file.papertrans.cn/702/701927/701927.mp4
概述Includes supplementary material:
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: OpenMP: Heterogenous Execution and Data Movements; 11th International W Christian Terboven,Bronis R. de Supinski,Matthias  Conference proce
描述.This book constitutes the refereed proceedings of the 11th International Workshop on OpenMP, held in Aachen, Germany, in October 2015. ..The 19 technical full papers presented were carefully reviewed and selected from 22 submissions. The papers are organized in topical sections on applications, accelerator applications, tools, extensions, compiler and runtime, and energy..
出版日期Conference proceedings 2015
關(guān)鍵詞accelerators; data parallelism; GPU; multicore; multiprocessing; ting methodologies; concurrency; dependenc
版次1
doihttps://doi.org/10.1007/978-3-319-24595-9
isbn_softcover978-3-319-24594-2
isbn_ebook978-3-319-24595-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2015
The information of publication is updating

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Experiences of Using the OpenMP Accelerator Model to Port DOE Stencil Applicationsator model provides a set of directives for users to specify semantics related to accelerators so that compilers and runtime systems can automatically handle repetitive and error-prone accelerator programming tasks, including code transformations, work scheduling, data management, reduction, and so
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False Sharing Detection in OpenMP Applications Using OMPT APIfalse sharing can be turned on and off. We run these programs both with and without false sharing and collect a set of hardware performance event counts using OMPT. We use the collected data to train a binary classifier. We test the trained classifier using the NAS Parallel Benchmark applications. O
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Markus Joppich,Dirk Schmidl,Anthony M. Bolger,Torsten Kuhlen,Bj?rn Usadel
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Joachim Protze,Ignacio Laguna,Dong H. Ahn,John Del Signore,Ariel Burton,Martin Schulz,Matthias S. Mü
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