Abstract
Scheduling is a widely used method in parallel computing, which assigns tasks to several compute resources of the parallel environments. In this article, we consider parallel tasks as the basic entities to be scheduled onto a heterogeneous execution platform consisting of multicores of different architecture. A parallel task has an internal potential parallelism which allows a parallel execution for example on multicore processors of different type. The assignment of tasks to different multicores of a heterogeneous execution platform may lead to different execution times for the same parallel tasks. Thus, the scheduling of parallel tasks onto a heterogeneous platform is more complex and provides more choices for the assignment and for finding the most efficient schedule. Search-based methods seem to be a promising approach to solve such complex scheduling problems. In this article, we propose a new task scheduling method HP* to solve the problem of scheduling parallel tasks onto heterogeneous platforms. Furthermore, we propose a cost function that reduces the search space of the algorithm. In performance measurements, the scheduling results of HP* are compared to several existing scheduling methods. Performance results with different benchmark tasks are shown to demonstrate the improvements achieved by HP*.
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Acknowledgments
This work was supported by the German Ministry of Science and Education (BMBF) project “SeASiTe”, Grant No. 01IH16012A/B and the German Research Foundation (DFG), Federal Cluster of Excellence EXC 1075 “MERGE”.
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Dietze, R., Rünger, G. (2020). Search-Based Scheduling for Parallel Tasks on Heterogeneous Platforms. In: Schwardmann, U., et al. Euro-Par 2019: Parallel Processing Workshops. Euro-Par 2019. Lecture Notes in Computer Science(), vol 11997. Springer, Cham. https://doi.org/10.1007/978-3-030-48340-1_26
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DOI: https://doi.org/10.1007/978-3-030-48340-1_26
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