ILAN: The Interference- and Locality-Aware NUMA Scheduler

Abstract

Non-Uniform Memory Access (NUMA) systems are increasingly common as the goto processor architecture for parallel computing within the field of High-Performance Computing (HPC). Similarly, OpenMP is the de-facto standard runtime for enabling parallelism. However, the default OpenMP runtime does not account for interference or data locality aspects, leading to performance degradations on NUMA systems where these effects become magnified. To address these challenges, this thesis proposes ILAN, an interference- and data locality-aware NUMA scheduler integrated into the LLVM OpenMP runtime, specifically targeting the taskloop construct. ILAN utilizes hardware topology information to enable a more structured task distribution strategy compared to the default OpenMP tasking scheduler, the work stealing scheduler, yielding improved data locality. Furthermore, the ILAN scheduler utilizes moldability to incorporate interference awareness, dynamically reducing the number of OpenMP threads to mitigate the effects of interference while further improving data locality. Performance evaluation using the NAS Parallel Benchmarks, Matrix Multiplication, and LULESH on a multi-socket NUMA platform demonstrates an average speedup of 10%, with a maximum speedup of 46%, compared to the default OpenMP work stealing scheduler.