Improving the performance of communication-intensive parallel applications executing on clusters

X. Qin, Hong Jiang
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引用次数: 1

Abstract

Summary form only given. Clusters have emerged as a primary and cost-effective infrastructure for parallel applications, including communication-intensive applications that transfer a large amount of data among nodes of a cluster via the interconnection network. Conventional load balancers have been proven effective in increasing the utilization of CPU, memory, and disk I/O resources in a cluster. However, most of the existing load balancing schemes ignore network resources, leaving open the opportunity for significant performance bottleneck to form for communication-intensive parallel applications due to unevenly distributed communication load. To remedy this problem, we propose a communication-aware load balancing technique that is capable of improving the performance of communication-intensive applications by increasing the effective utilization of network resources in clusters. To facilitate the proposed load-balancing scheme, we introduce a behavior model for parallel applications with large requirements of CPU, memory, network, and disk 170 resources. The proposed load-balancing scheme can make full use of this model to quickly and accurately determine the load induced by a variety of parallel applications. Simulation results on executing a diverse set of both synthetic bulk synchronous and real parallel applications on a cluster show that the proposed scheme can significantly improve the performance both in slowdown and turn-around time over three existing schemes by up to 206% (with an average of 74%) and 235% (with an average of 82%), respectively.
提高在集群上执行通信密集型并行应用程序的性能
只提供摘要形式。集群已经成为并行应用程序(包括通过互连网络在集群的节点之间传输大量数据的通信密集型应用程序)的主要和经济有效的基础设施。传统的负载平衡器已被证明在提高集群中CPU、内存和磁盘I/O资源的利用率方面是有效的。然而,大多数现有的负载平衡方案都忽略了网络资源,由于通信负载分布不均,使得通信密集型并行应用程序有可能形成显著的性能瓶颈。为了解决这个问题,我们提出了一种通信感知负载平衡技术,该技术能够通过提高集群中网络资源的有效利用率来提高通信密集型应用程序的性能。为了促进所提出的负载平衡方案,我们为具有大量CPU、内存、网络和磁盘170资源需求的并行应用程序引入了一个行为模型。所提出的负载均衡方案可以充分利用该模型,快速准确地确定各种并行应用引起的负载。在集群上执行各种合成批量同步和实际并行应用程序的仿真结果表明,与现有的三种方案相比,所提出的方案在减速和周转时间方面的性能分别提高了206%(平均74%)和235%(平均82%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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