Scalar Waving: Improving the Efficiency of SIMD Execution on GPUs

Abstract

GPUs take advantage of uniformity in program control flow and utilize SIMD execution to obtain execution efficiency. In SIMD execution, threads are batched into SIMD groups to share a common program counter and execute identical instructions on SIMD pipelines. Previous research has shown that there is a significant number of scalar instructions - instructions where different threads in a SIMD group execute using the same input operands and generate the exact same output - present in a range of applications. GPUs eliminate redundant fetches and decodes by utilizing a shared common pipeline front-end. However, most GPUs do not handle scalar instruction efficiently, allowing these instructions to be redundantly executed by the threads in a SIMD group. In this paper, we propose to use scalar execution to eliminate redundant execution of scalar instructions. We introduce scalar waving as a mechanism to batch scalar operations possessing the same PC and execute them as a group on SIMD lanes for efficiency. We also propose simultaneous execution of dynamically-formed scalar waves with SIMD groups to overcome the under-utilization of SIMD lanes when encountering divergence. We evaluate our work using 22 different GPU benchmarks taken from 4 different benchmark suites. We evaluate a range of configurations using timing simulation. Our results show that scalar waving can obtain up to a 25% improvement in performance on average. Our experiments also provide insight into the amount of performance gain that we can expect with scalar waving as a function of the scalar content, occupancy, and memory characteristics of the target application.