Today's compilers offer a huge number of transformation options to choose among and this choice can significantly impact on the performance of the code being optimized. Not only the selection of compiler options represents a hard problem to be solved, but also the ordering of the phases is adding further complexity, making it a long standing problem in compilation research. This paper presents an innovative approach for tackling the compiler phase-ordering problem by using predictive modeling. The proposed methodology enables i) to efficiently explore compiler exploration space including optimization permutations and repetitions and ii) to extract the application dynamic features to predict the next-best optimization to be applied to maximize the performance given the current status. Experimental results are done by assessing the proposed methodology with utilizing two different search heuristics on the compiler optimization space and it demonstrates the effectiveness of the methodology on the selected set of applications. Using the proposed methodology on average we observed up to 4% execution speedup with respect to LLVM standard baseline.

Predictive modeling methodology for compiler phase-ordering

ASHOURI, AMIR HOSSEIN;PALERMO, GIANLUCA;SILVANO, CRISTINA
2016-01-01

Abstract

Today's compilers offer a huge number of transformation options to choose among and this choice can significantly impact on the performance of the code being optimized. Not only the selection of compiler options represents a hard problem to be solved, but also the ordering of the phases is adding further complexity, making it a long standing problem in compilation research. This paper presents an innovative approach for tackling the compiler phase-ordering problem by using predictive modeling. The proposed methodology enables i) to efficiently explore compiler exploration space including optimization permutations and repetitions and ii) to extract the application dynamic features to predict the next-best optimization to be applied to maximize the performance given the current status. Experimental results are done by assessing the proposed methodology with utilizing two different search heuristics on the compiler optimization space and it demonstrates the effectiveness of the methodology on the selected set of applications. Using the proposed methodology on average we observed up to 4% execution speedup with respect to LLVM standard baseline.
2016
Proceeding PARMA-DITAM '16 Proceedings of the 7th Workshop on Parallel Programming and Run-Time Management Techniques for Many-core Architectures and the 5th Workshop on Design Tools and Architectures For Multicore Embedded Computing Platforms - ACM International Conference Proceeding Series
9781450340526
Autotuning; Compilers; Machine Learning; Phase-ordering; Software
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1026282
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