Reproducible Performance Evaluation of OpenMP and SYCL Workloads under Noise Injection

Paper i proceeding, 2025

Performance instability caused by unpredictable system noise remains a persistent challenge in high-performance and parallel computing. This work presents a reproducible methodology to characterize this variability through noise injection, tested using workloads implemented in OpenMP and SYCL to compare their performance resilience under noisy conditions. We design a noise injector that captures real system traces and replays the deltas as controlled noises. Using this approach, we evaluate multiple mitigation efforts, that is, thread pinning, housekeeping core isolation, and simultaneous multithreading (SMT) toggling, under both default and noise-injected executions. Experiments with two benchmarks (N-body, Babelstream) and one mini-application (MiniFE) across two processor platforms show that while OpenMP consistently achieves higher raw performance, SYCL tends to exhibit greater resilience in noisy environments. Mitigation effectiveness varies with workload characteristics, system configuration, and noise intensity, with housekeeping core isolation offering the clearest benefits, particularly in high-noise scenarios.