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ALONA: Automatic Loop Nest Approximation with Reconstruction and Space Pruning
Citation key 10.1007/978-3-030-85665-6_1
Author Daniel Maier and Biagio Cosenza and Ben Juurlink
Title of Book Euro-Par 2021: Parallel Processing
Pages 3–18
Year 2021
ISBN 978-3-030-85665-6
Address Cham
Editor Leonel Sousa and Nuno Roma and Pedro Tomás
Publisher Springer International Publishing
Abstract Approximate computing comprises a large variety of techniques that trade the accuracy of an application's output for other metrics such as computing time or energy cost. Many existing approximation techniques focus on loops such as loop perforation, which skips iterations for faster, approximated computation. This paper introduces ALONA, a novel approach for automatic loop nest approximation based on polyhedral compilation. ALONA's compilation framework applies a sequence of loop approximation transformations, generalizes state-of-the-art perforation techniques, and introduces new multi-dimensional approximation schemes. The framework includes a reconstruction technique that significantly improves the accuracy of the approximations and a transformation space pruning method based on Barvinok's counting that removes inaccurate approximations. Evaluated on a collection of more than twenty applications from PolyBench/C, ALONA discovers new approximations that are better than state-of-the-art techniques in both approximation accuracy and performance.
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