On privatization of variables for data-parallel execution

Abstract

Privatization of data is an important technique that has been used by compilers to parallelize loops by eliminating storage-related dependences. When a compiler partitions computations based on the ownership of data, selecting a proper mapping of privatizable data is crucial to obtaining the benefits of privatization. This paper presents a novel framework for privatizing scalar and array variables in the context of a data-driven approach to parallelization. We show that there are numerous alternatives available for mapping privatized variables and the choice of mapping can significantly affect the performance of the program. We present an algorithm that attempts to preserve parallelism and minimize communication overheads. We also introduce the concept of partial privatization of arrays that combines data partitioning and privatization, and enables efficient handling of a class of codes with multi-dimensional data distribution that was not previously possible. Finally, we show how the ideas of privatization apply to the execution of control flow statements as well. An implementation of these ideas in the pHPF prototype compiler for High Performance Fortran on the IBM SP2 machine has shown impressive results.