Abstract
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. © Springer-Verlag Berlin Heidelberg 1999.
Cite
CITATION STYLE
Cohen, A., & Lefebvre, V. (1999). Storage mapping optimization for parallel programs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1685 LNCS, pp. 375–382). Springer Verlag. https://doi.org/10.1007/3-540-48311-x_49
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