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Development of scalable application codes requires an understanding and exploitation of the locality and parallelism in the computation. This is typically achieved through op-timizations by the programmer to match the application characteristics to the architectural features exposed by the parallel programming model. Partitioned address space program-ming models such as MPI foist a process-centric view of the parallel system, increasing the complexity of parallel programming. Typical global address space models provide a shared memory view that greatly simplifies programming. But the simplified models abstract away the locality information, precluding optimized implementations. In this work, we present techniques to reorganize program execution to optimize locality and parallelism, with little effort from the programmer. For regular loop-based programs operating on dense multi-dimensional arrays, we pro-pose an automatic parallelization technique that attempts to determine a parallel schedule in which all processes can start execution in parallel. When the concurrent tiled iteration space inhibits such execution, we present techniques to re-enable it. This is an alternativ