Learning-based Dynamic Pinning of Parallelized Applications in Many-Core Systems
share
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 compared to the Linux operating system scheduler.
READ FULL TEXT