Towards Robust Data-Driven Parallel Loop Scheduling Using Bayesian Optimization

Khu-rai Kim, Youngjae Kim, Sungyong Park

Abstract: Efficient parallelization of loops is critical to improving the performance of high-performance computing applications. Many classical parallel loop scheduling algorithms have been developed to increase parallelization efficiency. Recently, workload-aware methods were developed to exploit the structure of workloads. However, both classical and workload-aware scheduling methods lack what we call robustness. That is, most of these scheduling algorithms tend to be unpredictable in terms of performance or have specific workload patterns they favor. This causes application developers to spend additional efforts in finding the best suited algorithm or tune scheduling parameters. This paper proposes Bayesian Optimization augmented Factoring Self-Scheduling (BO FSS), a robust data-driven parallel loop scheduling algorithm. BO FSS is powered by Bayesian Optimization (BO), a machine learning based optimization algorithm. We augment a classical scheduling algorithm, Factoring Self-Scheduling (FSS), into a robust adaptive method that will automatically adapt to a wide range of workloads. To compare the performance and robustness of our method, we have implemented BO FSS and other loop scheduling methods on the OpenMP framework. A regret-based metric called performance regret is also used to quantify robustness. Extensive benchmarking results show that BO FSS performs fairly well in most workload patterns and is also very robust relative to other scheduling methods. BO FSS achieves an average of 4% performance regret. This means that even when BO FSS is not the best performing algorithm on a specific workload, it stays within a 4 percentage points margin of the best performing algorithm.