G-Scalar: Cost-Effective Generalized Scalar Execution Architecture for Power-Efficient GPUs

Zhenhong Liu, Syed Gilani, Murali Annavaram, Nam Sung Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The GPU has provide higher throughput by integrating more execution resources into a single chip without unduly compromising power efficiency. With the power wall challenge, however, increasing the throughput will require significant improvement in power efficiency. To accomplish this goal, we propose G-Scalar, a cost-effective generalized scalar execution architecture for GPUs in this paper. G-Scalar offers two key advantages over prior architectures supporting scalar execution for only non-divergent arithmetic/logic instructions. First, G-Scalar is more power-efficient as it can also support scalar execution of divergent and special-function instructions, the fraction of which in contemporary GPU applications has notably increased. Second, G-Scalar is less expensive as it can share most of its hardware resources with register value compression, of which adoption has been strongly promoted to reduce high power consumption of accessing the large register file. Compared with the baseline and previous scalar architectures, G-Scalar improves power efficiency by 24% and 15%, respectively, at a negligible cost.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE 23rd Symposium on High Performance Computer Architecture, HPCA 2017
PublisherIEEE Computer Society
Pages601-612
Number of pages12
ISBN (Electronic)9781509049851
DOIs
StatePublished - May 5 2017
Event23rd IEEE Symposium on High Performance Computer Architecture, HPCA 2017 - Austin, United States
Duration: Feb 4 2017Feb 8 2017

Publication series

NameProceedings - International Symposium on High-Performance Computer Architecture
ISSN (Print)1530-0897

Other

Other23rd IEEE Symposium on High Performance Computer Architecture, HPCA 2017
CountryUnited States
CityAustin
Period2/4/172/8/17

Keywords

  • GPU
  • register file
  • register file compression
  • scalar execution

ASJC Scopus subject areas

  • Hardware and Architecture

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