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Title:Method-Specific Dynamic Compilation using Logistic Regression
Authors: John Cavazos ; Michael O'Boyle
Date:Oct 2006
Publication Title:International Conference on Object-Oriented Programming, Systems, Languages, and Applications, Portland, WA
Publisher:ACM Press
Publication Type:Conference Paper Publication Status:Published
Volume No:41 (10) Page Nos:12
DOI:10.1145/1167473.1167492 ISBN/ISSN:1-59593-348-4
Determining the best set of optimizations to apply to a program has been a long standing problem for compiler writers. To reduce the complexity of this task, existing approaches typically apply the same set of optimizations to all procedures within a program, without regard to their particular structure. This paper develops a new method-specific approach that automatically selects the best optimizations on a per method basis within a dynamic compiler. Our approach uses the machine learning technique of logistic regression to automatically derive a predictive model that determines which optimizations to apply based on the features of a method. This technique is implemented in the Jikes RVM Java JIT compiler. Using this approach we reduce the average total execution time of the SPECjvm98 benchmarks by 29%. When the same heuristic is applied to the DaCapo+ benchmark suite, we obtain an average 33% reduction over the default level O2 setting.
2006 by The University of Edinburgh. All Rights Reserved
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Bibtex format
author = { John Cavazos and Michael O'Boyle },
title = {Method-Specific Dynamic Compilation using Logistic Regression},
book title = {International Conference on Object-Oriented Programming, Systems, Languages, and Applications, Portland, WA},
publisher = {ACM Press},
year = 2006,
month = {Oct},
volume = {41 (10)},
pages = {12},
doi = {10.1145/1167473.1167492},
url = {},

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