GPU performance and power tuning using regression trees

Wenhao Jia, Elba Garza, Kelly A. Shaw, Margaret Rose Martonosi

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

GPU performance and power tuning is difficult, requiring extensive user expertise and time-consuming trial and error. To accelerate design tuning, statistical design space exploration methods have been proposed. This article presents Starchart, a novel design space partitioning tool that uses regression trees to approach GPU tuning problems. Improving on prior work, Starchart offers more automation in identifying key design trade-offs and models design subspaces with distinctly different behaviors. Starchart achieves good model accuracy using very few random samples: less than 0.3% of a given design space; iterative sampling can more quickly target subspaces of interest.

Original languageEnglish (US)
Article number13
JournalACM Transactions on Architecture and Code Optimization
Volume12
Issue number2
DOIs
StatePublished - May 1 2015

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Hardware and Architecture

Keywords

  • Decision tree
  • Design space exploration
  • GPGPU
  • Statistical modeling

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