The search‐based scheduling algorithm HP* for parallel tasks on heterogeneous platforms

Abstract

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*. K E Y W O R D S heterogeneous platforms, parallel tasks, pruning techniques, search-based scheduling This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Task scheduling on heterogeneous multiprocessor systems through coherent data allocation

“…Task scheduling problems are usually solved with heuristics or meta‐heuristics 6,20‐22 . Heuristics are often based on greedy selection strategies, and their performance is not very agile in complex situations 6,17,18,23‐27 . Topcuoglu et al 23 proposed two classic scheduling algorithms: heterogeneous earliest‐finish‐time and critical‐path‐on‐a‐processor.…”