Power-efficient computing for compute-intensive GPGPU applications

Abstract

The peak performance of graphics processing units (GPUs) has traditionally been increased by increasing the number of compute resources and/or their frequency. However, these approaches significantly increase the power consumption of GPUs. Consequently, modern high-performance GPUs are power constrained and must employ more power efficient approaches for performance improvements in future processors. In this paper we propose three power-efficient techniques for improving the performance of GPUs. First, we observe that many GPGPU applications are integer instruction intensive. For such applications, we propose to utilize the fused multiply-add (FMA) units to fuse dependent integer instructions into a composite instruction, improving power efficiency and performance by reducing the number of fetched/executed instructions. Secondly, GPUs often perform computations that are duplicated across multiple threads. We dynamically detect such instructions and execute them in a separate scalar pipeline. Finally, the register file bandwidth in GPUs is a critical resource that is optimized for 32-bit instruction operands. However, many operands require considerably fewer bits for accurate representation and computations. We propose a sliced GPU architecture that improves performance of the GPU by dual-issuing instructions to two 16-bit execution slices. Overall, our techniques result in more than a 25% (geometric mean) power efficiency improvement.