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Titlebook: High Performance Computing; 33rd International C Rio Yokota,Michèle Weiland,Carsten Trinitis Conference proceedings 2018 Springer Internati

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书目名称High Performance Computing
副标题33rd International C
编辑Rio Yokota,Michèle Weiland,Carsten Trinitis
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: High Performance Computing; 33rd International C Rio Yokota,Michèle Weiland,Carsten Trinitis Conference proceedings 2018 Springer Internati
描述.This book constitutes the refereed proceedings of the 33rd International Conference, ISC High Performance 2018, held in Frankfurt, Germany, in June 2018...The 20 revised full papers presented in this book were carefully reviewed and selected from 81 submissions. The papers cover the following topics: Resource Management and Energy Efficiency; Performance Analysis and Tools; Exascale Networks; Parallel Algorithms..
出版日期Conference proceedings 2018
关键词Artificial intelligence; Computer architectures; Dependable and fault-tolerant systems and networks; Di
版次1
doihttps://doi.org/10.1007/978-3-319-92040-5
isbn_softcover978-3-319-92039-9
isbn_ebook978-3-319-92040-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing AG, part of Springer Nature 2018
The information of publication is updating

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/h/image/426289.jpg
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Applicability of the ECM Performance Model to Explicit ODE Methods on Current Multi-core Processorsntation variants to support the variant selection. We investigate the accuracy of the prediction for different ODEs and different hardware platforms and show that the prediction is able to reliably select a set of fast variants and, thus, to limit the search space for possible later empirical tuning
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Machine Learning Based Parallel I/O Predictive Modeling: A Case Study on Lustre File Systemsed models are better at prediction when compared with application-partitioned or unpartitioned models. We highlight modeling techniques that are robust to the outliers that can occur in production parallel file systems. Using the developed metrics and modeling approach, we provide insights into the
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