A Framework for Exploiting Data and Functional Parallelism on Distributed Memory Multicomputers
A Framework for Exploiting Data and Functional Parallelism on Distributed Memory Multicomputers
复制标题
在分布式内存多计算机上利用数据和功能并行性的框架
DOI：
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发表时间：
1994
期刊：
影响因子：
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通讯作者：
P. Banerjee
中科院分区：
文献类型：
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作者：
S. Ramaswamy;S. Sapatnekar;P. Banerjee
摘要：最近的研究工作表明，集成功能和数据并行的好处，无论是使用纯数据并行或纯功能并行。本文的工作提出了一个理论框架，用于根据程序中可用的功能和数据并行性来决定给定程序的良好执行策略。该框架是基于多计算机系统的计算和通信成本函数的形式的假设。我们提出了这些成本的数学函数，并表明这些函数是现实的。该框架还需要规范的可用功能和数据并行给定的问题。为此，我们开发了一个图形编程工具。目前，我们已经在Thinking Machines CM-5和Intel Paragon上使用三个基准程序测试了我们的方法。结果表明，该方法是非常有效的，可以提供一个两到三倍的加速比的方法，只使用数据并行。功能和数据并行，宏流程图，图形编程，处理器分配，调度，DMM
Abstract : Recent research efforts have shown the benefits of integrating functional and data parallelism over using either pure data parallelism or pure functional parallelism. The work in this paper presents a theoretical framework for deciding on a good execution strategy for a given program based on the available functional and data parallelism in the program. The framework is based on assumptions about the form of computation and communication cost functions for multicomputer systems. We present mathematical functions for these costs and show that these functions are realistic. The framework also requires specification of the available functional and data parallelism for a given problem. For this purpose, we have developed a graphical programming tool. Currently, we have tested our approach using three benchmark programs on the Thinking Machines CM-5 and Intel Paragon. Results presented show that the approach is very effective and can provide a two- to three-fold increase in speedups over approaches using only data parallelism. Functional and data parallelism, Macro dataflow graphs, Graphical programming, Processor allocation, Scheduling, DMM