Performance Enhancement by Memory Reduction

In this paper, we propose a technique to reduce the virtual memory required to store program data. Specifically, we present an optimal algorithm to combine loop shifting, loop fusion and array contraction to reduce the temporary array storage required to execute a collection of loops. Memory reduction is formulated as a net.work How problem, which is solved by the proposed algorithm in polynomial time. When applied to 20 benchmark programs on two platforms, our technique reduces the memory requirement, counting both the data and the code, by 50% on average. The transformed programs gain a speedup of 1.57 on average, due to the reduced working set and, consequently, the improved data locality. In the best case, a maximum speedup of 41.3 is achieved for one of the bencbmark programs.

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