High-Performance Computing systems rely on the software’s capability to be highly parallelized in individual computing tasks. However, even with a high parallelization level, poor scheduling can lead to long runtimes; this scheduling is in itself an NP-hard problem. Therefore, it is our interest to use a heuristic approach, particularly Cellular Processing Algorithms (CPA), which is a novel metaheuristic framework for optimization. This framework has its foundation in exploring the search space by multiple Processing Cells that communicate to exploit the search and in the individual stagnation detection mechanism in the Processing Cells. In this paper, we proposed using a Greedy Randomized Adaptive Search Procedure (GRASP) to look for promising task execution orders; later, a CPA formed with Iterated Local Search (ILS) Processing Cells is used for the optimization. We assess our approach with a high-performance ILS state-of-the-art approach. Experimental results show that the CPA outperforms the previous ILS in real applications and synthetic instances.

中文翻译：

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异构系统优先约束任务列表调度的GRASP和基于迭代局部搜索的元胞处理算法

高性能计算系统依靠该软件的功能在各个计算任务中高度并行化。但是，即使并行化级别很高，调度不当也会导致较长的运行时间。这种调度本身就是一个NP难题。因此，我们的兴趣是使用启发式方法，特别是蜂窝处理算法（CPA），它是一种用于优化的新颖的元启发式框架。该框架的基础是通过多个处理单元探索搜索空间，这些处理单元进行通信以利用搜索，并具有处理单元中的单个停滞检测机制。在本文中，我们提出使用贪婪随机自适应搜索程序（GRASP）来寻找有前途的任务执行顺序。后来，由迭代本地搜索（ILS）处理单元形成的CPA用于优化。我们使用高性能的ILS最新方法评估我们的方法。实验结果表明，在实际应用和综合实例中，CPA均优于以前的ILS。