Enhancing Data Reuse in Cache Contention Aware Thread Scheduling on GPGPU

Abstract

GPGPUs have been widely adopted as throughput processing platforms for modern big-data and cloud computing. Attaining a high performance design on a GPGPU requires careful tradeoffs among various design concerns. Data reuse, cache contention, and thread level parallelism, have been demonstrated as three imperative performance factors for a GPGPU. The correlated performance impacts of these factors pose non-trivial concerns when scheduling threads on GPGPUs. This paper proposes a three-staged scheduling scheme to co-schedule the threads with consideration of the three factors. The experiment results on a set of irregular parallel applications, when compared with previous approaches, have demonstrated up to 70% execution time improvement.