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Estinating Minimum Parallel Execution Time of Loops with Loop-Carried Dependencies

机译:估计具有循环依赖的循环的最小并行执行时间

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摘要

The article proposes a method of calculating the minimum parallel execution time of loops. The loop is completely unrolled and the task graph is considered. By assigning the execution time of the corresponding task and the communication time related to the dependency as costs to the nodes and edges of the task graph, the cost of the critical path which is the path for which the total cost of the nodes and edges composing the path is the maximum, gives the minimum parallel execution time of the loops. It is not desirable, however, in considering the computation time and the required memory capacity, to unroll the loops completely. The method presented here formulates the problem as the integer programming problem, and calcu- lates the cost of the critical path without unrolling the loops at all. In this study, a branch and bound algorithm that solves the integer programming problem is implemented. The implemented algorithm unrolls the loops and determines the critical path on the task graph. As a result of evaluation using Livermore's benchmark kernel, it is verified that the implemented algorithm can be executed in a sufficiently practical time, regardless of the size (number of iterations) of the loops.
机译:本文提出了一种计算循环的最小并行执行时间的方法。循环已完全展开,并考虑了任务图。通过将相应任务的执行时间和与依存关系相关的通信时间分配为任务图的节点和边缘的成本,关键路径的成本即构成节点和边缘总成本的路径该路径为最大,给出循环的最小并行执行时间。但是,在考虑计算时间和所需的存储容量时,不希望完全展开循环。此处介绍的方法将问题表述为整数编程问题,并计算关键路径的成本而根本不展开循环。在这项研究中,实现了一种解决整数规划问题的分支定界算法。所实现的算法将展开循环并确定任务图上的关键路径。作为使用Livermore基准内核进行评估的结果,已验证了所实现的算法可以在足够实际的时间内执行,而与循环的大小(迭代次数)无关。

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