在使用压缩的网格连接多计算机中分配非连续处理器

S. Bani-Mohammad, I. Ababneh, Mohammad Yassen
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引用次数: 0

摘要

在非连续分配中,作业请求可以分成更小的部分,分配可能不相邻的空闲子网格,而不是总是等待所请求的大小和形状的单个子网格可用。提高连续性条件有望减少处理器碎片并提高系统利用率。但是,消息传递的距离可能很长,因此增加了通信开销,特别是争用。额外的通信开销取决于如何对分配请求进行分区并将其分配给空闲的子网格。本文针对二维网格网络提出了一种新的非连续处理器分配策略,即压缩非连续处理器分配策略(CNCPA)。在提出的策略中,单个作业被压缩到分配的处理器内的多个空闲位置,其中剩余的可用处理器(空闲处理器)在系统中形成一个大的子网格。为了评估所提出的策略所取得的性能改进,并将其与已知的现有非连续分配策略进行比较,我们在虫洞路由和一对所有和近邻通信模式的假设下进行了大量的仿真实验。结果表明,该策略可以消除内部和外部碎片,减少通信开销,从而在作业周转时间和系统利用率方面提高性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Non-contiguous processor allocation in the mesh-connected multicomputers using compaction
In non-contiguous allocation, a job request can be split into smaller parts that are allocated possibly non-adjacent free sub-meshes rather than always waiting until a single sub-mesh of the requested size and shape is available. Lifting the contiguity condition is expected to reduce processor fragmentation and increase system utilization. However, the distances traversed by messages can be long, and as a result the communication overhead, especially contention, is increased. The extra communication overhead depends on how the allocation request is partitioned and assigned to free sub-meshes. In this paper, a new non-contiguous processor allocation strategy, referred to as A Compacting Non-Contiguous Processor Allocation Strategy (CNCPA), is suggested for the 2D mesh networks. In the proposed strategy, a single job is compacting into more than one free location within the allocated processors, where the remaining available processors (free processors) form a large sub-mesh in the system. To evaluate the performance improvement achieved by the proposed strategy and compare it against well-known existing non-contiguous allocation strategies, we conduct extensive simulation experiments under the assumption of wormhole routing and the one-to-all and near neighbor communication patterns. The results show that the proposed strategy can eliminate both the internal and external fragmentation and reduce the communication overhead and hence improve performance in terms of job turnaround time and system utilization.
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