Title: Efficient Dynamic Mapping of Parallel Applications to NUMA Architectures by Reinforcement Learning

Abstract: We present a dynamic framework for mapping threads and data of parallel applications to computational cores/memory nodes of parallel non-uniform memory architecture (NUMA) systems. We use a feedback-based mechanism where the performance of each thread is collected and used to drive the reinforcement-learning policy of assigning affinities of threads/data to CPU cores/memory nodes. The proposed framework can address different optimisation criteria, such as maximum processing speed and minimum speed variance. We demonstrate that we can achieve an improvement of 12% in execution time compared to the default Linux operating system scheduling/mapping of threads under varying availability of resources (e.g. when multiple applications are running on the same system).