SAWS: Selective Asymmetry-Aware Work-Stealing for Asymmetric Multi-core Architectures

Abstract

Asymmetric Multi-Core (AMC) architectures, where cores in different CPUs have different performance and power consumption, have been widely used from large-scale datacenters to mobile smart-phones for their high performance as well as energy efficiency. However, existing task scheduling policies often result in the poor performance of parallel programs on emerging AMC architectures due to the unbalanced workload, the severe shared cache misses and remote memory accesses. To solve this problem, we propose a Selective Asymmetry-aware Work-Stealing (SAWS) runtime system, which can reduce remote memory accesses while balancing workload across asymmetric cores. SAWS consists of an asymmetric-aware task allocator and a selective work-stealing scheduler. The asymmetric-aware task allocator properly distributes the tasks to asymmetric CPUs so that most tasks can access data from local memory node and the workload is balanced according to the computational ability of different CPUs. After that, the selective work-stealing scheduler is used to further balance the workload at runtime and adjust the frequencies of asymmetric cores. Our real-system experimental results show that SAWS improves the performance of memory-bound programs up to 59.3% compared with traditional work-stealing schedulers in AMC architectures.