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Predictive Modeling in a Polyhedral Optimization Space (2011)
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| Venue: | INTERNATIONAL SYMPOSIUM ON CODE GENERATION AND OPTIMIZATION (CGO'11) |
| Citations: | 18 - 3 self |
BibTeX
@MISC{Park11predictivemodeling,
author = {Eunjung Park and Louis-noël Pouchet and John Cavazos and Albert Cohen and et al.},
title = { Predictive Modeling in a Polyhedral Optimization Space},
year = {2011}
}
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Abstract
Significant advances in compiler optimization have been made in recent years, enabling many transformations such as tiling, fusion, parallelization and vectorization on imperfectly nested loops. Nevertheless, the problem of finding the best combination of loop transformations remains a major challenge. Polyhedral models for compiler optimization have demonstrated strong potential for enhancing program performance, in particular for compute-intensive applications. But existing static cost models to optimize polyhedral transformations have significant limitations, and iterative compilation has become a very promising alternative to these models to find the most effective transformations. But since the number of polyhedral optimization alternatives can be enormous, it is often impractical to iterate over a significant fraction of the entire space of polyhedrally transformed variants. Recent research has focused on iterating over this search space either with manually-constructed heuristics or with automatic but very expensive search algorithms (e.g., genetic algorithms) that can eventually find good points in the polyhedral space. In this paper, we propose the use of machine learning to address the problem of selecting the best polyhedral optimizations. We show that these models can quickly find high-performance program variants in the polyhedral space, without resorting to extensive empirical search. We introduce models that take as input a characterization of a program based on its dynamic behavior, and predict the performance of aggressive high-level polyhedral transformations that includes tiling, parallelization and vectorization. We allow for a minimal empirical search on the target machine, discovering on average 83 % of the searchspace-optimal combinations in at most 5 runs. Our end-to-end framework is validated using numerous benchmarks on two multi-core platforms.
Keyphrases
polyhedral optimization space predictive modeling code generation international symposium author manuscript polyhedral space compiler optimization polyhedral optimization alternative iterative compilation polyhedral transformation polyhedral optimization static cost model compute-intensive application manually-constructed heuristic significant limitation search space numerous benchmark multi-core platform target machine aggressive high-level polyhedral transformation dynamic behavior effective transformation significant fraction genetic algorithm good point high-performance program variant many transformation program performance strong potential abstract significant advance polyhedral model promising alternative major challenge searchspace-optimal combination minimal empirical search recent research empirical search recent year loop transformation entire space end-to-end framework expensive search algorithm
