Quantifying Data Locality in Dynamic Parallelism in GPUs

Abstract

Dynamic parallelism (DP) is a new feature of emerging GPUs that allows new kernels to be generated and scheduled from the device-side (GPU) without the host-side (CPU) intervention. To efficiently support DP, one of the major challenges is to saturate the GPU processing elements and provide them with the required data in a timely fashion. In this paper, we first conduct a limit study on the performance improvements that can be achieved by hardware schedulers that are provided with accurate data reuse information. We next propose LASER, a Locality-Aware SchedulER, where the hardware schedulers employ data reuse monitors to help make scheduling decisions to improve data locality at runtime. Experimental results on 16 benchmarks show that LASER, on an average, can improve performance by 11.3%.