Titlebook: Algorithms and Architectures for Parallel Processing; 18th International C Jaideep Vaidya,Jin Li Conference proceedings 2018 Springer Natur

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书目名称Algorithms and Architectures for Parallel Processing影响因子(影响力)




书目名称Algorithms and Architectures for Parallel Processing影响因子(影响力)学科排名




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书目名称Algorithms and Architectures for Parallel Processing网络公开度学科排名




书目名称Algorithms and Architectures for Parallel Processing被引频次




书目名称Algorithms and Architectures for Parallel Processing被引频次学科排名




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书目名称Algorithms and Architectures for Parallel Processing年度引用学科排名




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书目名称Algorithms and Architectures for Parallel Processing读者反馈学科排名

憎恶

Adaptive Data Sampling Mechanism for Process Objectve to the current underlying distribution of data in data stream. For finding appropriate data in big data stream to model process object, an adaptive data sampling mechanism is proposed in this paper. Experiments demonstrate the effectiveness of the proposed adaptive data sampling mechanism for process object.

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短程旅游

Mindfulness

0302-9743 lected from numerous submissions. The papers are organized in topical sections on Distributed and Parallel Computing; High Performance Computing; Big Data and Information Processing; Internet of Things and Cloud Computing; and Security and Privacy in Computing..978-3-030-05050-4978-3-030-05051-1Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Bürgergesellschaft und Demokratie a scalable structure to record the access information of the vertices on each machine. Second, we prune unnecessary inter-machine communication for previously accessed vertices by checking the records. Evaluation results show that the performance of our method is at least six times higher than that of the original implementation of parallel BFS.

密切关系

hemophilia

PruX: Communication Pruning of Parallel BFS in the Graph 500 Benchmark a scalable structure to record the access information of the vertices on each machine. Second, we prune unnecessary inter-machine communication for previously accessed vertices by checking the records. Evaluation results show that the performance of our method is at least six times higher than that of the original implementation of parallel BFS.

FATAL

https://doi.org/10.1007/978-3-322-84276-3s the fastest in computing speed benefiting from its optimization for CPU, but it suffers from long communication delay due to the dependency on MapReduce framework. The insights and conclusions from our evaluation provides significant reference for improving resource utility of supercomputer resources in distributed deep learning.

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