Assessing the Performance of the SRR Loop Scheduler with Irregular Workloads
Résumé
The input workload of an irregular application must be evenly distributed among
its threads to enable cutting-edge performance. To address this need in OpenMP, several loop
scheduling strategies were proposed. While having this ever-increasing number of strategies at dis-
posal is helpful, it has become a non-trivial task to select the best one for a particular application.
Nevertheless, this challenge becomes easier to be tackled when existing scheduling strategies are
extensively evaluated. Therefore, in this paper, we present a performance and scalability eval-
uation of the recently-proposed loop scheduling strategy named Smart Round-Robin (SRR). To
deliver a comprehensive analysis, we coupled a kernel benchmarking technique with several rigorous
statistical tools, and considered OpenMP’s Static and Dynamic loop schedulers as our baselines.
Our results unveiled that SRR performs better on irregular applications with symmetric workloads
and coarse-grained parallelization, achieving up to 1.9x and 1.5x speedup over OpenMP’s Static
and Dynamic schedulers on synthetic kernels, respectively. On a N-Body Simulations application
kernel, SRR delivered 2.48x better performance in contrast to OpenMP’s Dynamic scheduler.
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