Unifying data and control transformations for distributed shared-memory machines

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Abstract
We present a unified approach to locality optimization that employs both data and control transformations. Data transformations include changing the array layout in memory. Control transformations involve changing the execution order of programs. We have developed new techniques for compiler optimizations for distributed shared-memory machines, although the same techniques can be used for sequential machines with a memory hierarchy. Our compiler optimizations are based on an algebraic representation of data mappings and a new data locality model. We present a pure data transformation algorithm and an algorithm unifying data and control transformations. While there has been much work on control transformations, the opportunities for data transformations have been largely neglected. In fact, data transformations have the advantage of being applicable to programs that cannot be optimized with control transformations. The unified algorithm, which performs data and control transformations simultaneously , offers improvement over optimizations obtained by applying data and control transformations separately. The experimental results using a set of applications on a parallel machine show that the new optimizations improve performance significantly. These results are further analyzed using locality metrics with instrumentation and simulation.

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