Optimizing Latency under Throughput Requirements for Streaming Applications on Cluster Execution

F. Guirado, A. Ripoll, C. Roig, E. Luque
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引用次数: 13

Abstract

Parallelism in applications that act on a stream of input data can be exploited with two different approaches, spatial and temporal. In this paper we propose a new task mapping algorithm, called EXPERT, to exploit temporal parallelism efficiently when the streaming application is running in a pipeline fashion. We compare the performance of spatial and temporal approaches, in terms of latency and throughput for a video compression application. The results show that the pipeline execution with the task assignment provided by EXPERT algorithm, significantly overcomes spatial parallelism. Additionally, this temporal parallelism presents better scalability results when the dimension of the problem is augmented
在吞吐量要求下优化集群执行流应用程序的延迟
处理输入数据流的应用程序中的并行性可以通过两种不同的方法来利用:空间和时间。在本文中,我们提出了一种新的任务映射算法,称为EXPERT,以便在流应用程序以管道方式运行时有效地利用时间并行性。在视频压缩应用程序的延迟和吞吐量方面,我们比较了空间和时间方法的性能。结果表明,在EXPERT算法提供的任务分配下,流水线执行明显克服了空间并行性。此外,当问题的维度增加时,这种时间并行性提供了更好的可伸缩性结果
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