Autotuning algorithmic choice for input sensitivity
Author(s)Ding, Yufei; Ansel, Jason; Veeramachaneni, Kalyan; Shen, Xipeng; O'Reilly, Una-May; Amarasinghe, Saman P.; ... Show more Show less
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A daunting challenge faced by program performance autotuning is input sensitivity, where the best autotuned configuration may vary with different input sets. This paper presents a novel two-level input learning algorithm to tackle the challenge for an important class of autotuning problems, algorithmic autotuning. The new approach uses a two-level input clustering method to automatically refine input grouping, feature selection, and classifier construction. Its design solves a series of open issues that are particularly essential to algorithmic autotuning, including the enormous optimization space, complex influence by deep input features, high cost in feature extraction, and variable accuracy of algorithmic choices. Experimental results show that the new solution yields up to a 3x speedup over using a single configuration for all inputs, and a 34x speedup over a traditional one-level method for addressing input sensitivity in program optimizations.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Proceedings of the 36th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2015)
Association for Computing Machinery (ACM)
Yufei Ding, Jason Ansel, Kalyan Veeramachaneni, Xipeng Shen, Una-May O’Reilly, and Saman Amarasinghe. 2015. Autotuning algorithmic choice for input sensitivity. In Proceedings of the 36th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2015). ACM, New York, NY, USA, 379-390.
Author's final manuscript