Titlebook: Euro-Par 2023: Parallel Processing; 29th International C José Cano,Marios D. Dikaiakos,Rizos Sakellariou Conference proceedings 2023 The Ed

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书目名称Euro-Par 2023: Parallel Processing影响因子(影响力)




书目名称Euro-Par 2023: Parallel Processing影响因子(影响力)学科排名




书目名称Euro-Par 2023: Parallel Processing网络公开度




书目名称Euro-Par 2023: Parallel Processing网络公开度学科排名




书目名称Euro-Par 2023: Parallel Processing被引频次




书目名称Euro-Par 2023: Parallel Processing被引频次学科排名




书目名称Euro-Par 2023: Parallel Processing年度引用




书目名称Euro-Par 2023: Parallel Processing年度引用学科排名




书目名称Euro-Par 2023: Parallel Processing读者反馈




书目名称Euro-Par 2023: Parallel Processing读者反馈学科排名

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Die Getriebe der Werkzeugmaschinenodel uses the underlying hardware efficiently takes a lot of effort. Knowledge about inference characteristics can help to find the right match so that enough resources are given to the model, but not too much. We have developed a DL Inference Performance Predictive Model (DIPPM) that predicts the i

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Organe des aktiven Bewegungsapparates,. This issue becomes especially relevant since MPI, the de-facto standard for inter-process communication, lacks proper fault management functionalities. Past efforts produced extensions to the MPI standard enabling fault management, including ULFM. While providing powerful tools to handle faults, U

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J. Brodersen,A. Maximow,J. Schafferext few years. A fundamental research problem in live streaming is bitrate coordination, which selects the proper upload/download bitrate for multiple participants in the system to maximize the users’ quality of experience (QoE). The existing bitrate adaptation methods fail to achieve optimal perfor

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V. Bisceglie,A. Juhész-Schäfferristics, the . algorithm groups tasks by packs of . tasks of similar execution times, and schedules first the packs with the largest differences. It turns out to be very performant in practice, but only few studies have been conducted on its theoretical properties. We derive novel analytical results

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https://doi.org/10.1007/978-3-7091-2196-2ng that might result in CPU throttling or out-of-memory errors. We take a stochastic approach that models the uncertainty of tasks’ resource requirements by random variables. We focus on a little-explored case of items, each having a Bernoulli distribution that corresponds to tasks that are either i