An Analytical Method for Parallelization of Recursive Functions

Programming with parallel skeletons is an attractive framework because it encourages programmers to develop e cient and portable parallel programs. However, extracting parallelism from sequential speci cations and constructing e cient parallel programs using the skeletons are still di cult tasks. In this paper, we propose an analytical approach to transforming recursive functions on general recursive data structures into compositions of parallel skeletons. Using static slicing, we have de ned a classi cation of subexpressions based on their data-parallelism. Then, skeleton-based parallel programs are generated from the classi cation. To extend the scope of parallelization, we have adopted more general parallel skeletons which do not require the associativity of argument functions. In this way, our analytical method can parallelize recursive functions with complex data ows.