There are many methods existing for nested loop partitioning; however, most of them perform poorly when partitioning loops with non-uniform dependences. This paper proposes a generalized and optimized loop partitioning mechanism to exploit parallelism from nested loops with non-uniform dependences. Our approach, based on dependence convex theory, divides a loop into variable-size partitions. Furthermore, the proposed algorithm partitions a nested loop by using the copy-renaming and optimized partitioning techniques in order to minimize the number of parallel regions of the iteration space, outperforming other previous mechanisms for partitioning nested loops with non-uniform dependences.
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