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Combined iterative and model-driven optimization in an automatic parallelization framework

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6 Author(s)
Pouchet, L. ; Ohio State Univ., Columbus, OH, USA ; Bondhugula, U. ; Bastoul, C. ; Cohen, A.
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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.

Published in:

High Performance Computing, Networking, Storage and Analysis (SC), 2010 International Conference for

Date of Conference:

13-19 Nov. 2010

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