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Titlebook: Euro-Par 2017: Parallel Processing; 23rd International C Francisco F. Rivera,Tomás F. Pena,José C. Cabaleir Conference proceedings 2017 Spr

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发表于 2025-3-21 18:57:29 | 显示全部楼层 |阅读模式
书目名称Euro-Par 2017: Parallel Processing
副标题23rd International C
编辑Francisco F. Rivera,Tomás F. Pena,José C. Cabaleir
视频video
概述Includes supplementary material:
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Euro-Par 2017: Parallel Processing; 23rd International C Francisco F. Rivera,Tomás F. Pena,José C. Cabaleir Conference proceedings 2017 Spr
描述This book constitutes the proceedings of the 23rd International Conference on Parallel and Distributed Computing, Euro-Par 2017, held in Santiago de Compostela, Spain, in August/September 2017. The 50 revised full papers presented together with 2 abstract of invited talks and 1 invited paper were carefully reviewed and selected from 176 submissions. The papers are organized in the following topical sections: support tools and environments; performance and power modeling, prediction and evaluation; scheduling and load balancing; high performance architectures and compilers; parallel and distributed data management and analytics; cluster and cloud computing; distributed systems and algorithms; parallel and distributed programming, interfaces and languages; multicore and manycore parallelism; theory and algorithms for parallel computation and networking; prallel numerical methods and applications; and accelerator computing.
出版日期Conference proceedings 2017
关键词computer architecture; program processors; semantics; parallel programming; libraries; cloud computing; wi
版次1
doihttps://doi.org/10.1007/978-3-319-64203-1
isbn_softcover978-3-319-64202-4
isbn_ebook978-3-319-64203-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing AG 2017
The information of publication is updating

书目名称Euro-Par 2017: Parallel Processing影响因子(影响力)




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发表于 2025-3-21 20:52:54 | 显示全部楼层
Euro-Par 2017: Parallel Processing978-3-319-64203-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
发表于 2025-3-22 02:32:54 | 显示全部楼层
Zur Finanzierung von Kleinbahnen,ffline analysis. Instead, applications must analyze and reduce data online so as to output only those results needed to answer target scientific question(s). This change in focus complicates application and experiment design and introduces algorithmic, implementation, and programming model challenge
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Für einen schlanken, starken Staattal footprint. This includes to minimise total power consumption or to minimise the power issued from non-renewable energy sources. Hence, the performance requirements of the client’s applications are either totally overlooked or strictly enforced..To encourage profitable sustainability in data cent
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Der Problemkomplex der Finanzwirtschaft,orious, developers increasingly resort to empirical performance modeling as a viable alternative, which learns performance models from a limited amount of performance measurements. Although powerful automatic techniques exist for this purpose, they usually struggle with the situation where performan
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Die Finite-Elemente-Methode auf dem PCent algorithms more challenging, the need to understand application communication patterns has become increasingly relevant. This paper presents details of the design and evaluation of a communication-monitoring infrastructure developed in the Open MPI software stack that can expose a dynamically co
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Die Lebenstätigkeit der Flechtenlogy are presented to assess these guidelines. Measurement results from a large, InfiniBand-based cluster, the Vienna Scientific Cluster (VSC), as well as from a small commodity cluster computer are shown and discussed to illustrate the methodology and to gain first insights into the performance of
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Ordnung: Lichenes kryoblasti Kbr.unication patterns important to other graph analytic and irregular applications. Unlike simulations, it has no floating point arithmetic and is dominated by small memory transactions within and between computing nodes. In this work, we focus on the highly scalable HipMer assembler and identify the d
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https://doi.org/10.1007/978-3-531-91049-9 NVIDIA GPU. Their heterogeneous nature, as well as their wide operating frequency range, make it hard for application developers to reason about performance and determine which optimizations are worth pursuing. This paper attempts to inform developers’ choices by characterizing the platforms’ perfo
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,Zusammenfassung und Schlußfolgerungen, Extra-P, a tool for automated performance modeling, uses statistical methods to automatically generate, from a small number of performance measurements, models that can be used to predict performance where no measurements are available. However, the current version requires the manual pre-configura
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