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Titlebook: Euro-Par 2018: Parallel Processing; 24th International C Marco Aldinucci,Luca Padovani,Massimo Torquati Conference proceedings 2018 Springe

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书目名称Euro-Par 2018: Parallel Processing
副标题24th International C
编辑Marco Aldinucci,Luca Padovani,Massimo Torquati
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Euro-Par 2018: Parallel Processing; 24th International C Marco Aldinucci,Luca Padovani,Massimo Torquati Conference proceedings 2018 Springe
描述This book constitutes the proceedings of the 24th International Conference on Parallel and Distributed Computing, Euro-Par 2018, held in Turin, Italy, in August 2018. The 57 full papers presented in this volume were carefully reviewed and selected from 194 submissions. They were organized in topical sections named: support tools and environments; performance and power modeling, prediction and evaluation; scheduling and load balancing; high performance architecutres 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 methods and tools; theory and algorithms for parallel computation and networking; parallel numerical methods and applications; and accelerator computing for advanced applications. .
出版日期Conference proceedings 2018
关键词artificial intelligence; cloud computing; cluster and cloud computing; computer networks; High-Performan
版次1
doihttps://doi.org/10.1007/978-3-319-96983-1
isbn_softcover978-3-319-96982-4
isbn_ebook978-3-319-96983-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2018
The information of publication is updating

书目名称Euro-Par 2018: Parallel Processing影响因子(影响力)




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https://doi.org/10.1007/978-3-662-41287-9 its time and space complexity, including the . to compress the computation graph representation, the . combined with . to reduce the number of unnecessary traversals, and . to limit the range of data that is monitored. The impact of these optimizations is demonstrated for nine benchmark programs wr
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https://doi.org/10.1007/978-3-642-94368-3oint and encodes this information in program instructions. Further, it uses a runtime optimization that increases the accuracy of power state of registers. We implemented the proposed ideas using GPGPU-Sim simulator and evaluated them on 21 kernels from several benchmarks suites. We observe that whe
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Birgit Kampmann,Bernhard Keller,Frank Wagnerr source and study their behavior when multiple such blocks are operating together pushing the power source to its limit. Our results show that choosing the correct CPU governor alone is not sufficient but tuning the DVFS of different resources is necessary to achieve the best performance with minim
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Die Freie Waldorfschule - eine Mogelpackung?ting from a performance model as a multi-class queuing network, we formulate a model-predictive control problem which can be efficiently solved by linear programming. A validation performed on a shared virtualized environment hosting two real applications shows that only a combined vertical and hori
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Die Infrastruktur der Feuerwehren,cterization is likely to be realized during some execution and all the processors are therefore going to be needed, for the rest of the time the unneeded processors could be idled in low-energy “sleep” mode, or used for executing non-real time work in the background. In this paper we propose an algo
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