Scheduling is a widely used method in parallel computing, which assigns tasks to compute resources of a parallel environments. In this article, we consider independent parallel tasks to be scheduled onto a heterogeneous execution platform consisting of a set of multicores of different architecture. Each parallel task has an internal potential parallelism which allows a parallel execution on any multicore processors. However, the execution time may differ due a different computation speed of different multicores. In this article, we propose a new search‐based scheduling algorithm Heterogeneous Parallel task scheduling based on A* (called HP*) to solve the problem of scheduling independent parallel tasks onto heterogeneous multicore platforms. Specifically, we propose a heuristic cost function needed for an informed search. Also, three pruning techniques are proposed, which are shown to significantly reduce the search space of HP*. Performance measurements on a heterogeneous platform are performed and the results of HP* are compared to scheduling results of other popular scheduling methods. The performance results with benchmark tasks from the SPLASH‐3 benchmark suite demonstrate the good scheduling results and the improvements achieved by HP*.

中文翻译：

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基于搜索的异构平台并行任务调度算法HP*

调度是并行计算中广泛使用的方法，它将任务分配给并行环境的计算资源。在本文中，我们考虑将独立的并行任务调度到由一组不同架构的多核组成的异构执行平台上。每个并行任务都有一个内部潜在的并行性，允许在任何多核处理器上并行执行。但是，由于不同多核的计算速度不同，执行时间可能会有所不同。在本文中，我们提出了一种新的基于搜索的调度算法——基于 A*（称为 HP*）的异构并行任务调度，以解决将独立并行任务调度到异构多核平台上的问题。具体来说，我们提出了一个知情搜索所需的启发式成本函数。还，提出了三种修剪技术，它们被证明可以显着减少 HP* 的搜索空间。在异构平台上执行性能测量，并将 HP* 的结果与其他流行调度方法的调度结果进行比较。SPLASH-3 基准测试套件的基准测试任务的性能结果表明 HP* 取得了良好的调度结果和改进。