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This paper presents reduction recognition and parallel code generation strategies for distributed-memory multiprocessors. We describe techniques to recognize a broad range of implicit reduction operations, including those involving statements at multiple loop nesting levels and intermixed with conditional control flow. We introduce two new optimizations: factoring which increases data locality for SUM and PRODUCT reductions, and index encoding which enables a single global communication to accomplish both an extreme value reduction and an extreme value location reduction. We have implemented these techniques in the dHPF compiler for High Performance Fortran (HPF). We evaluate their effectiveness experimentally by compiling several reduction benchmarks with dHPF and two commercial HPF compilers, and comparing the performance of the generated code on an IBM SP2. Our results show that our recognition techniques are more powerful and that our index encoding and factoring optimizations can improve performance by a factor of two where they apply.