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Titlebook: Euro-Par 2016: Parallel Processing Workshops; Euro-Par 2016 Intern Frédéric Desprez,Pierre-François Dutot,Josef Weide Conference proceeding

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书目名称Euro-Par 2016: Parallel Processing Workshops
副标题Euro-Par 2016 Intern
编辑Frédéric Desprez,Pierre-François Dutot,Josef Weide
视频videohttp://file.papertrans.cn/317/316537/316537.mp4
概述Includes supplementary material:
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Euro-Par 2016: Parallel Processing Workshops; Euro-Par 2016 Intern Frédéric Desprez,Pierre-François Dutot,Josef Weide Conference proceeding
描述This book constitutes the proceedings of the workshops of the 23rd International Conference on Parallel and Distributed Computing, Euro-Par 2016, held in Grenoble, France in August 2016. .  The 65 full papers presented were carefully reviewed and selected from 95 submissions.. The volume includes the papers from the following workshops: Euro-EDUPAR (Second European Workshop on Parallel and Distributed Computing Education for Undergraduate Students) – HeteroPar 2016 (the 14th International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Platforms) – IWMSE (5th International Workshop on Multicore Software Engineering) – LSDVE (Fourth Workshop on Large-Scale Distributed Virtual Environments) -  PADABS (Fourth Workshop on Parallel and Distributed Agent-Based Simulations) – PBio (Fourth International Workshop on Parallelism in Bioinformatics) – PELGA (Second Workshop on Performance Engineering for Large-Scale Graph Analytics) – REPPAR (Third International Workshop on Reproducibility in Parallel Computing) – Resilience (9th Workshop in Resilience in High Performance Computing in Clusters, Clouds, and Grids) – ROME (Fourth Workshop on Runtime and Operating
出版日期Conference proceedings 2017
关键词programming languages, compilers, interpreters; parallel programming languages; distributed programmin
版次1
doihttps://doi.org/10.1007/978-3-319-58943-5
isbn_softcover978-3-319-58942-8
isbn_ebook978-3-319-58943-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing AG 2017
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https://doi.org/10.1007/978-3-7091-9998-5speed up may be reached on a multi-GPU platform (relatively to the mono-GPU case) and, as a very preliminary but promising result, that the approach can be effectively used to handle heterogenous architectures composed of a multicore chip and multiple GPUs. We expect that these results will pave the
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Die Festigkeitseigenschaften des Magnesiums,available cores) possibly enhanced with GPUs. We illustrate our discussion with the . sparse hybrid solver relying on the . and . dense and sparse direct libraries, respectively. Interestingly, this two-level MPI+task design furthermore provides extra flexibility for controlling the number of subdom
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Die Eisenbahntarife. Allgemeines,e meta programming techniques to synthesize multiple versions of a parallel task during the compilation process..To demonstrate its effectiveness, we evaluate the impact of our approach on the performance of a series of eight task parallel benchmarks. For those, our approach achieves substantial spe
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Quellen-und Literaturverzeichnis,ation and discuss the potential and limits of this approach in terms of productivity and effectiveness in comparison with more common parallelization techniques. Although at an early stage of development, preliminary results show the potential of the parallel programming model that we investigate in
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Experiences with Teaching a Second Year Distributed Computing Courseely: trade-offs, failures, concurrency and synchronization, performance. The paper presents the details of this approach arguing that the use of suitable abstractions allows for a rewarding learning experience that helps students familiarize with and appreciate the challenges of distributed computin
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Distributed In-GPU Data Cache for Document-Oriented Data Store via PCIe over 10 Gbit Ethernetpression match query throughput with up to three NVIDIA GeForce GTX 980 devices connected to a host via PCIe over 10 GbE. We demonstrate that the communication overhead of remote GPU devices is small and can be compensated by a great flexibility to add more GPU devices via a network. We also show th
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