Knowledge-Based Adaptive Self-Scheduling

Loop scheduling scheme plays a critical role in the efficient execution of programs, especially loop dominated applications. This paper presents KASS, a knowledge-based adaptive loop scheduling scheme. KASS consists of two phases: static partitioning and dynamic scheduling. To balance the workload, the knowledge of loop features and the capabilities of processors are both taken into account using a heuristic approach in static partitioning phase. In dynamic scheduling phase, an adaptive self-scheduling algorithm is applied, in which two tuning parameters are set to control chunk sizes, aiming at load balancing and minimizing synchronization overhead. In addition, we extend KASS to apply on loop nests and adjust the chunk sizes at runtime. The experimental results show that KASS performs 4.8% to 16.9% better than the existing selfscheduling schemes, and up to 21% better than the affinity scheduling scheme.