A Lightweight Method for Handling Control Divergence in GPGPUs

Abstract

At present, graphics processing units (GPUs) has been widely used for scientific and high performance acceleration in the general purpose computing area, which is inseparable from the SIMT (Single-Instruction, Multiple-Thread) execution model. With SIMT, GPUs can fully utilize the advantages of SIMD parallel computing. However, when threads in a warp do not follow the same execution path, control divergence generates and affects the hardware utilization. In response to this problem, warp regrouping method has been proposed to combine threads executing the same branch path, which can significantly improve thread-level parallelism. But it is found that not all warps can be regrouped effectively because that may introduce a lot of unnecessary overheads, limiting further performance improvement. In this paper, we analyze the source of overheads and propose a lightweight warp regrouping method --- Partial Warp Regrouping (PWR) that controls the scope of reorganization and avoids most of the unnecessary warp regrouping by setting thresholds. In this method, it also can reduce the complexity of hardware design. Our experimental results show that this mechanism can improve the performance by 12% on average and up to 27% compared with immediate post-dominator.