Storage Mapping Optimization for Parallel Programs

Data dependences are known to hamper efficient parallelization of programs. Memory expansion is a general method to remove dependences in assigning distinct memory locations to dependent writes. Parallelization via memory expansion requires both moderation in the expansion degree and efficiency at run-time. We present a general storage mapping optimization framework for imperative programs, applicable to most loop nest parallelization techniques.

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