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Task assignment in heterogeneous computing systems using an effective iterated greedy algorithm

机译:使用有效的迭代贪婪算法的异构计算系统中的任务分配

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摘要

A fundamental issue affecting the performance of a parallel application running on a heterogeneous computing system is the assignment of tasks to the processors in the system. The task assignment problem for more than three processors is known to be NP-hard, and therefore satisfactory suboptimal solutions obtainable in an acceptable amount of time are generally sought. This paper proposes a simple and effective iterative greedy algorithm to deal with the problem with goal of minimizing the total sum of execution and communication costs. The main idea in this algorithm is to improve the quality of the assignment in an iterative manner using results from previous iterations. The algorithm first uses a constructive heuristic to find an initial assignment and iteratively improves it in a greedy way. Through simulations over a wide range of parameters, we have demonstrated the effectiveness of our algorithm by comparing it with recent competing task assignment algorithms in the literature.
机译:影响在异构计算系统上运行的并行应用程序性能的根本问题是将任务分配给系统中的处理器。已知用于三个以上处理器的任务分配问题是NP难题,因此通常需要在可接受的时间内获得令人满意的次优解决方案。本文提出了一种简单有效的迭代贪婪算法来解决该问题,目标是将执行和通信成本的总和最小化。该算法的主要思想是使用先前迭代的结果以迭代的方式提高分配的质量。该算法首先使用一种构造启发式算法来查找初始分配,然后以贪婪的方式迭代地对其进行改进。通过对各种参数的仿真,我们通过与文献中最新的竞争任务分配算法进行比较,证明了该算法的有效性。

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