Combined Iterative and Model-driven Optimization in an Automatic Parallelization Framework

Abstract : Today's multi-core era places significant demands on an optimizing compiler, which must parallelize programs, exploit memory hierarchy, and leverage the ever-increasing SIMD capabilities of modern processors. Existing model-based heuristics for performance optimization used in compilers are limited in their ability to identify profitable parallelism/locality trade-offs and usually lead to sub-optimal performance. To address this problem, we distinguish optimizations for which effective model-based heuristics and profitability estimates exist, from optimizations that require empirical search to achieve good performance in a portable fashion. We have developed a completely automatic framework in which we focus the empirical search on the set of valid possibilities to perform fusion/code motion, and rely on model-based mechanisms to perform tiling, vectorization and parallelization on the transformed program. We demonstrate the effectiveness of this approach in terms of strong performance improvements on a single target as well as performance portability across different target architectures.
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Louis-Noël Pouchet, Uday Bondhugula, Cédric Bastoul, Albert Cohen, Jagannathan Ramanujam, et al.. Combined Iterative and Model-driven Optimization in an Automatic Parallelization Framework. Conference on Supercomputing (SC'10), Nov 2010, New Orleans, LA, United States. ⟨inria-00551067⟩

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