An Efficient Locality-Aware Task Assignment Algorithm for Minimizing Shared Cache Contention

Abstract

Task scheduling can improve the performance of parallel execution through optimizing the utilization of on-chip computing resources, and thus it has been widely studied. Most of the previous work uses data access locality to predict cache behaviors for task scheduling, but usually suffering accuracy and computational time complexity issues. This paper proposes an efficient task assignment algorithm to minimize the contention for shared caches on multi-core processors among parallel independent process level tasks. The proposed algorithm leverages the property of footprint to approximately estimate the locality parameter of parallel tasks, choosing the best grouping of tasks with minimum locality value in a quick way for task assignment. The calculation time is therefore significantly reduced and the algorithm complexity is O(nlog2n). Meanwhile, the algorithm accuracy is very high. On an Intel 8 cores dual-processor system, the experimental results show that the task assignment algorithm achieves over 99% of the actual optimal performance on average and outperforms the default Linux task scheduling method by an average of over 5% for two sets of different parallel tasks.