Learning-based dynamic pinning of parallelized applications in many-core systems

Authors Georgios C. Chasparis
Michael Roßbory
Vladimir Janjic
Kevin Hammond
Editors
Title Learning-based dynamic pinning of parallelized applications in many-core systems
Booktitle Proceedings of the 27th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP 2019)
Type in proceedings
Publisher IEEE
ISBN 978-1-7281-1644-0
DOI 10.1109/EMPDP.2019.8671569
Month February
Year 2019
Pages 1-8
SCCH ID# 18014
Abstract

This paper introduces a reinforcement-learning based resource allocation framework for dynamic placement of threads of parallel applications to Non-Uniform Memory Access (NUMA) many-core systems. We propose a two-level learning-based decision making process, where at the first level each thread independently decides on which group of cores (NUMA node) it will execute, and on the second level it decides to which particular core from the group it will be pinned. Additionally, a novel performance-based learning dynamics is introduced to handle measurement noise and rapid variations in the performance of the threads. Experiments on a 24-core system show the improvement of up to 16% in the execution time of parallel applications under our framework, compared to the Linux operating system scheduler.