Loop fusion improves data locality and reduces synchronization in data-parallel applications. However, loop fusion is not always legal. Even when legal, fusion may introduce loop-carried dependences which prevent parallelism. In addition, performance losses result from cache conflicts in fused loops. In this paper, we present new techniques to: (1) allow fusion of loop nests in the presence of fusion-preventing dependences, (2) maintain parallelism and allow the parallel execution of fused loops with minimal synchronization, and (3) eliminate cache conflicts in fused loops. We describe algorithms for implementing these techniques in compilers. The techniques are evaluated on a 56-processor KSR2 multiprocessor and on a 18-processor Convex SPP-1000 multiprocessor. The results demonstrate performance improvements for both kernels and complete applications. The results also indicate that careful evaluation of the profitability of fusion is necessary as more processors are used.
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