Autotuning PolyBench benchmarks with LLVM Clang/Polly loop optimization pragmas using Bayesian optimization. (8th November 2021)

• Record Type: Journal Article
• Authors: Wu, Xingfu; Kruse, Michael; Balaprakash, Prasanna; Finkel, Hal; Hovland, Paul; Taylor, Valerie; Hall, Mary
• Other Names: Wright Steven A. guestEditor.; Solak Serdar guestEditor.; Kilimci Zeynep Hilal guestEditor.; Eken Süleyman guestEditor.; Fernandes Steven guestEditor.; Zhang Yu‐Dong guestEditor.; Tavares João Manuel R.S. guestEditor.
• Is Part Of: Concurrency and computation. Volume 34:Number 20(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-11-08
• Subjects: autotuning -- Clang -- loop transformation -- machine learning -- optimization -- Polly -- PolyBench benchmarks; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6683
• Languages: English
• ISSNs: 1532-0626

Abstract

Abstract: We develop a ytopt autotuning framework that leverages Bayesian optimization to explore the parameter space search and compare four different supervised learning methods within Bayesian optimization and evaluate their effectiveness. We select six of the most complex PolyBench benchmarks and apply the newly developed LLVM Clang/Polly loop optimization pragmas to the benchmarks to optimize them. We then use the autotuning framework to optimize the pragma parameters to improve their performance. The experimental results show that our autotuning approach outperforms the other compiling methods to provide the smallest execution time for the benchmarks syr2k, 3mm, heat‐3d, lu, and covariance with two large datasets in 200 code evaluations for effectively searching the parameter spaces with up to 170, 368 different configurations. We find that the Floyd–Warshall benchmark did not benefit from autotuning. To cope with this issue, we provide some compiler option solutions to improve the performance. Then we present loop autotuning without a user's knowledge using a simple mctree autotuning framework to further improve the performance of the Floyd–Warshall benchmark. We also extend the ytopt autotuning framework to tune a deep learning application.