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GenMAT: A General-Purpose Machine Learning-Driven Auto-Tuner for Heterogeneous Platforms
Event Type
Heterogeneous Systems
Parallel Programming Languages and Models
Parallel Programming Systems
Productivity Tools
System Software and Runtime Systems
Registration Categories
TimeFriday, 19 November 20218:31am - 8:55am CST
DescriptionTo assist the end-users in optimally deploying workloads on the heterogeneous environment with high productivity, a fundamental problem is to automatically find the best “variant” of an application—the implementation with the optimal configurations on the most suitable hardware resource resulting in the minimum runtime. We propose GenMAT, a portable tool for identifying the best variant of any application specified as a meta-program with exposed tunable parameters on any hardware. GenMAT automatically profiles the application by varying the exposed tunable parameters and trains a compact machine learning model to quickly predict the runtimes of numerous candidate variants to identify the best variant. We show that the GenMAT-selected variant has a runtime deviation within 3.5% of the true best variant in determining the best linear algebra library for matrix operations. GenMAT correctly ranks the runtimes of thousands of Halide schedules with an average Spearman’s rank correlation coefficient of 0.95.
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