Abstract
Heterogeneous parallel architectures present many challenges to application developers. One of the most important ones is the decision where to execute a specific task. As today’s systems are often dynamic in nature, this cannot be solved at design time. A solution is offered by runtime systems that employ dynamic scheduling algorithms. Still, the question which algorithm to use remains.
In this paper, we evaluate several dynamic scheduling algorithms on a real system using different benchmarks. To be able to use the algorithms on a real system, we integrate them into a task-based runtime system. The evaluation covers different heuristic classes: In immediate mode, tasks are scheduled in the order they arrive in the system, whereas in batch mode, all ready-to-execute tasks are considered during the scheduling decision. The results show that the Minimum Completion Time and the Min-Min heuristics achieve the overall best makespans. However, if additionally scheduling fairness has to be considered as optimization goal, the Sufferage algorithm seems to be the algorithm of choice.
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Becker, T., Karl, W., Schüle, T. (2019). Evaluating Dynamic Task Scheduling in a Task-Based Runtime System for Heterogeneous Architectures. In: Schoeberl, M., Hochberger, C., Uhrig, S., Brehm, J., Pionteck, T. (eds) Architecture of Computing Systems – ARCS 2019. ARCS 2019. Lecture Notes in Computer Science(), vol 11479. Springer, Cham. https://doi.org/10.1007/978-3-030-18656-2_11
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