Efficient GPGPU Computing with Cross-Core Resource Sharing and Core Reconfiguration

Abstract

GPUs are capable of running a variety of applications, however their generic parallel-architecture can lead to inefficient use of resources and reduced power efficiency, due to algorithmic or architectural constraints. In this work, taking inspiration from CGRAs (coarse-grained reconfigurable architectures), we demonstrate resource sharing and re-distribution as a solution that can be leveraged by reconfiguring the GPU on a kernel-by-kernel basis. We explore four different schemes that trade the number of active SMs (streaming multiprocessor) for increased occupancy and local memory resources per SM and demonstrate improved power and energy with limited impact to performance. Our most aggressive scheme, BigSM, is capable of saving energy by up to 54%, and 26% on an average.