- author = "Wenhao Jia and Elba Garza and Kelly A. Shaw and Margaret Martonosi",
- title = "{GPU} Performance and Power Tuning Using Regression Trees",
- journal = "ACM Transactions on Architecture and Code Optimization",
- year = "2015",
- volume = "12",
- number = "2",
- pages = "13:1--13:26",
- month = jul,
- keywords = "genetic algorithms, genetic programming, genetic improvement, GPU, SBSE, parallel computing, Design space exploration, GPGPU, statistical modeling, decision tree, Multiple Data Stream Architectures, Multiprocessors, Design, Measurement, Performance, breadth-first search graph algorithm",
- ISSN = "1544-3566",
- timestamp = "Wed, 17 Feb 2021 22:02:39 +0100",
-
biburl = "
https://dblp.org/rec/journals/taco/JiaGSM15.bib",
- bibsource = "dblp computer science bibliography, https://dblp.org",
-
URL = "https://collaborate.princeton.edu/en/publications/gpu-performance-and-power-tuning-using-regression-trees/",
-
DOI = "
10.1145/2736287",
- size = "26 pages",
- 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 behaviours. Starchart achieves good model accuracy using very few random samples: less than 0.3percent of a given design space; iterative sampling can more quickly target subspaces of interest.",
-
notes = "Not GP? nvidia, AMD, Semiconductor Research
Corporation
See also 'Analysis and Optimization Techniques for Massively Parallel Processors', Wenhao Jia, Ph.D. Thesis.
Princeton University",
