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Titlebook: High Performance Computing; 35th International C Ponnuswamy Sadayappan,Bradford L. Chamberlain,Hate Conference proceedings 2020 Springer Na

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發(fā)表于 2025-3-21 16:15:40 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱High Performance Computing
副標(biāo)題35th International C
編輯Ponnuswamy Sadayappan,Bradford L. Chamberlain,Hate
視頻videohttp://file.papertrans.cn/427/426296/426296.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: High Performance Computing; 35th International C Ponnuswamy Sadayappan,Bradford L. Chamberlain,Hate Conference proceedings 2020 Springer Na
描述This book constitutes the refereed proceedings of the 35th International Conference on High Performance Computing, ISC High Performance 2020, held in Frankfurt/Main, Germany, in June 2020.*.The 27 revised full papers presented were carefully reviewed and selected from 87 submissions. The papers cover a broad range of topics such as architectures, networks & infrastructure; artificial intelligence and machine learning; data, storage & visualization; emerging technologies; HPC algorithms; HPC applications; performance modeling & measurement; programming models & systems software...*The conference was held virtually due to the COVID-19 pandemic..Chapters?"Scalable Hierarchical Aggregation and Reduction Protocol (SHARP) Streaming-Aggregation Hardware Design and Evaluation", "Solving Acoustic Boundary Integral Equations Using High Performance Tile Low-Rank LU Factorization", "Scaling Genomics Data Processing with Memory-Driven Computing to Accelerate Computational Biology", "Footprint-Aware Power Capping for Hybrid Memory Based Systems", and "Pattern-Aware Staging for Hybrid Memory Systems" are?available open access under a Creative Commons Attribution 4.0 International License via link
出版日期Conference proceedings 2020
關(guān)鍵詞artificial intelligence; computer hardware; computer networks; computer programming; computer systems; di
版次1
doihttps://doi.org/10.1007/978-3-030-50743-5
isbn_softcover978-3-030-50742-8
isbn_ebook978-3-030-50743-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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Talita Perciano,Colleen Heinemann,David Camp,Brenton Lessley,E. Wes Bethelnciples of the cultural boycott and of supporting the apartheid regime. This case study invites us to critically assess the nature of the anti-apartheid movement in the Netherlands, which might be seen, first and foremost, as a ‘dialogue with the Afrikaners’.
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0302-9743 , held in Frankfurt/Main, Germany, in June 2020.*.The 27 revised full papers presented were carefully reviewed and selected from 87 submissions. The papers cover a broad range of topics such as architectures, networks & infrastructure; artificial intelligence and machine learning; data, storage & vi
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2 January 1968. The chapter illuminates how the comic mode helped to circulate an anti-apartheid message to a large audience, and, inadvertently, to subvert the explicit intent to protest apartheid. It demonstrates how the protest against a racist system of governance in South Africa highlights racism in Britain.
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Amir Raoofy,Roman Karlstetter,Dai Yang,Carsten Trinitis,Martin Schulzor BMW, which was due not only to the apartheid debate in Western Europe, but also to transnational ties with South African business circles. From the late 1970s onwards, multinational companies saw themselves as reform forces in South Africa, demanding an evolutionary overcoming of apartheid against revolutionary changes.
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Opportunities for Cost Savings with?In-Transit Visualizationases these efficiencies are enough to offset other costs (transfer, blocking, and additional nodes) to be cost effective overall. Finally, this work informs future studies, which can focus on choosing ideal configurations for in-transit processing that can consistently achieve cost efficiencies.
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Using High-Level Synthesis to Implement the Matrix-Vector Multiplication on FPGAIntel ARRIA10 FPGA. Some details are given to describe how the algorithm has been implemented using the QuickPlay HLS flow. Performance results are presented, in terms of sustained computational speed and resources used in the hardware implementation.
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