NDLI: Predicting unroll factors using supervised classification

Content Provider	IEEE Xplore Digital Library
Author	Stephenson, M. Amarasinghe, S.
Copyright Year	2005
Description	Author affiliation: Comput. Sci. & Artificial Intelligence Lab., MIT, Cambridge, MA, USA (Stephenson, M.; Amarasinghe, S.)
Abstract	Compilers base many critical decisions on abstracted architectural models. While recent research has shown that modeling is effective for some compiler problems, building accurate models requires a great deal of human time and effort. This paper describes how machine learning techniques can be leveraged to help compiler writers model complex systems. Because learning techniques can effectively make sense of high dimensional spaces, they can be a valuable tool for clarifying and discerning complex decision boundaries. In this work we focus on loop unrolling, a well-known optimization for exposing instruction level parallelism. Using the Open Research Compiler as a testbed, we demonstrate how one can use supervised learning techniques to determine the appropriateness of loop unrolling. We use more than 2,500 loops - drawn from 72 benchmarks - to train two different learning algorithms to predict unroll factors (i.e., the amount by which to unroll a loop) for any novel loop. The technique correctly predicts the unroll factor for 65% of the loops in our dataset, which leads to a 5% overall improvement for the SPEC 2000 benchmark suite (9% for the SPEC 2000 floating point benchmarks).
Starting Page	123
Ending Page	134
File Size	214869
Page Count	12
File Format	PDF
ISBN	076952298X
DOI	10.1109/CGO.2005.29
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-03-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pipeline processing Machine learning Humans Optimizing compilers Support vector machines Support vector machine classification History Computer science Artificial intelligence Laboratories
Content Type	Text
Resource Type	Article

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