Power-efficient computing for compute-intensive GPGPU applications

Syed Zohaib Gilani, Nam Sung Kim, Michael Schulte

Research output: Contribution to journalConference articlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)445-446
Number of pages2
JournalParallel Architectures and Compilation Techniques - Conference Proceedings, PACT
DOIs
StatePublished - Oct 22 2012
Externally publishedYes
Event21st International Conference on Parallel Architectures and Compilation Techniques, PACT 2012 - Minneapolis, MN, United States
Duration: Sep 19 2012Sep 23 2012

Keywords

  • GPU
  • Low-power
  • Power efficiency

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture

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