Reproducible Performance Variability Mitigation of OpenMP and SYCL Applications

Abstract

Performance variability caused by unpredictable system noise remains a persistent challenge in high-performance and parallel computing. This thesis presents a methodology for characterising such variability through reproducible noise injection, using three representative benchmarks implemented with OpenMP and SYCL. A custom noise injector was developed to capture real system traces, isolate average and outlier behaviours, and reinject the delta as controlled, reproducible noise. We evaluate and compare multiple mitigation strategies, such as thread pinning, use of housekeeping cores, and simultaneous multithreading (SMT) toggling, under both default and noise-injected conditions. Our experimental study spans three benchmarks (N-body, Babelstream, and MiniFE) executed on local Intel and AMD desktop processors, enabling a comprehensive analysis of mitigation effectiveness across platforms and workloads. Results indicate that while OpenMP consistently delivers higher raw performance, SYCL tends to be more resilient to noisy environments. The proposed noise injection framework facilitates more rigorous and repeatable assessment of parallel program behaviour under controlled perturbations. Although the effectiveness of mitigation strategies varies with workload characteristics, system configuration, and noise intensity, certain techniques, such as isolating housekeeping cores, show clear benefits, particularly in high-noise scenarios.