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Knowledge-Based Adaptive Self-Scheduling

Abstract : 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 self- scheduling schemes, and up to 21% better than the affinity scheduling scheme.
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Yizhuo Wang, Weixing Ji, Feng Shi, Qi Zuo, Ning Deng. Knowledge-Based Adaptive Self-Scheduling. 9th International Conference on Network and Parallel Computing (NPC), Sep 2012, Gwangju, South Korea. pp.22-32, ⟨10.1007/978-3-642-35606-3_3⟩. ⟨hal-01551352⟩



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