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Titlebook: Euro-Par 2020: Parallel Processing; 26th International C Maciej Malawski,Krzysztof Rzadca Conference proceedings 2020 Springer Nature Switz

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书目名称Euro-Par 2020: Parallel Processing
副标题26th International C
编辑Maciej Malawski,Krzysztof Rzadca
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Euro-Par 2020: Parallel Processing; 26th International C Maciej Malawski,Krzysztof Rzadca Conference proceedings 2020 Springer Nature Switz
描述.This book constitutes the proceedings of the 26th International Conference on Parallel and Distributed Computing, Euro-Par 2020, held in Warsaw, Poland, in August 2020. The conference was held virtually due to the coronavirus pandemic...The 39 full papers presented in this volume were carefully reviewed and selected from 158 submissions. They deal with parallel and distributed computing in general, focusing on support tools and environments; performance and power modeling, prediction and evaluation; scheduling and load balancing; high performance architectures and compilers; data management, analytics and machine learning; cluster, cloud and edge computing; theory and algorithms for parallel and distributed processing; parallel and distributed programming, interfaces, and languages; multicore and manycore parallelism; parallel numerical methods and applications; and accelerator computing..
出版日期Conference proceedings 2020
关键词artificial intelligence; cloud computing; computer networks; computer systems; distributed computer syst
版次1
doihttps://doi.org/10.1007/978-3-030-57675-2
isbn_softcover978-3-030-57674-5
isbn_ebook978-3-030-57675-2Series 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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https://doi.org/10.1007/978-3-642-71603-4pplications. Despite that, the process of correctly annotating complex applications is error-prone and may hinder the general adoption of these models. In this paper, we target the OmpSs-2 programming model and present a novel toolchain able to detect parallelization errors coming from non-compliant
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https://doi.org/10.1007/978-3-662-33233-7s, and machine learning executions require high performance computing platforms. Such infrastructures have been growing lately with the addition of thousands of newly designed components, calling their resiliency into question. It is crucial to solidify our knowledge on the way supercomputers fail.
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Die Gefahren der Vernetzung durch Vernetzungwhen a pipeline has reached its maximum performance capacity is generally a non-trivial task. Metrics exported at the software and at the hardware levels can provide insightful information about the current state of the system, but it can be difficult to interpret the value of a metric, or even to k
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,Historikerstreit — transnational, intermediate layer outputs (called activations) computed during the forward phase must be stored until the corresponding gradient has been computed in the backward phase. These memory requirements sometimes prevent to consider larger batch sizes and deeper networks, so that they can limit both conv
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