Pattern-Driven Automatic Parallelization

This paper describes a knowledge based system for automatic parallelization of a wide class of sequential numeric codes operating on vectors and dense matrices and for execution on distributed memory message passing multiprocessors Its main feature is a fast and powerful pattern recognition tool that locally identi es frequently occurring computations and programming concepts in the source code This tool works also for dusty deck codes that have been encrypted by former machine speci c code transformations Successful pattern recognition guides sophisticated code transformations including local algorithm replacement such that the parallelized code need not emerge from the sequential program structure by just parallelizing the loops It allows access to an expert s knowledge on useful parallel algorithms available machine speci c library routines and powerful program transformations The partially restored program semantics also supports local array alignment distribution and redistribution and allows for faster and more exact prediction of the performance of the parallelized target code than is usually possible