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 language | English (US) |
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Article number | 13 |
Journal | ACM Transactions on Architecture and Code Optimization |
Volume | 12 |
Issue number | 2 |
DOIs | |
State | Published - May 1 2015 |
All Science Journal Classification (ASJC) codes
- Software
- Information Systems
- Hardware and Architecture
Keywords
- Decision tree
- Design space exploration
- GPGPU
- Statistical modeling