Enhancing resource management through prediction-based policies
Document typeConference report
Rights accessOpen Access
Task-based programming models are emerging as a promising alternative to make the most of multi-/many-core systems. These programming models rely on runtime systems, and their goal is to improve application performance by properly scheduling application tasks to cores. Additionally, these runtime systems offer policies to cope with application phases that lack in parallelism to fill all cores. However, these policies are usually static and favor either performance or energy efficiency. In this paper, we have extended a task-based runtime system with a lightweight monitoring and prediction infrastructure that dynamically predicts the optimal number of cores required for each application phase, thus improving both performance and energy efficiency. Through the execution of several benchmarks in multi-/many-core systems, we show that our prediction-based policies have competitive performance while improving energy efficiency when compared to state of the art policies.
CitationNavarro, A. [et al.]. Enhancing resource management through prediction-based policies. A: International European Conference on Parallel and Distributed Computing. "Euro-Par 2020: Parallel Processing, 26th International Conference on Parallel and Distributed Computing: Warsaw, Poland, August 24–28, 2020: proceedings". Berlín: Springer, 2020, p. 493-509. ISBN 978-3-030-57675-2. DOI 10.1007/978-3-030-57675-2_31.
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