Evaluating dynamic task scheduling with priorities and adaptive aging in a task-based runtime system

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The high degree of parallelism of today’s computing systems often requires executing applications and their tasks in parallel due to a limited scaling capability of individual applications. In such scenarios, considering the differing importance of applications while scheduling tasks is done by assigning priorities to the tasks. However, priorities may lead to starvation in highly utilized systems. A solution is offered by aging mechanisms that raise the priority of long waiting tasks. As modern systems are often dynamic in nature, we developed a two-level aging mechanism and analyzed its effect in the context of 6 dynamic scheduling algorithms for heterogeneous systems. In the context of task scheduling, aging refers to a method that increases the priority of a task over its lifetime. We used a task-based runtime system to evaluate the mechanism on a real system in two scenarios. The results show a speed up of the average total makespan in 9 out of 12 conducted experiments when aging is used with the cost of additional waiting time for the applications/jobs with higher priority. However, the job/application with the highest priority is still finished first in all cases. Considering the scheduling algorithms, Minimum Completion Time, Sufferage, and Relative Cost benefit in both experiments by the aging mechanism. Additionally, no algorithm significantly dominates all other algorithms when total makespans are compared.




Becker, T., & Schüle, T. (2020). Evaluating dynamic task scheduling with priorities and adaptive aging in a task-based runtime system. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12155 LNCS, pp. 17–31). Springer. https://doi.org/10.1007/978-3-030-52794-5_2

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