With the advent of Distributed Memory Machines (DMMs) numerous work have been undertaken to ease the work of a programmer these systems. Data parallel languages like Fortran D, Vienna Fortran, High Performance Fortran and C+ allow the user to specify data distribution across processor with some directives, and the compiler for these language use the directives to compile the programme in to an SPMD code. There are number of old program which are still in use and rewriting them in to new data parallel languages is a costly effort. Most of the work on these parallelizing compilers concentrate on efficient data communication between the processors.With the advancement in technology, data communication time is also decreasing.This allows bigger programs to execute in the same time span.The resources of a DMM being finite puts a limit on the size of the problem that can be run. Improving the memory usage for a problem will hence allow us run bigger size problems. Further, as communication speed increases, the overhead caused by house-keeping computations like global index to local index transformation, and owner processor computation will degrade the performance of the resultant code. Hence a uniform and efficient method for these computations also becomes a necessity. We have implemented parallelizing parts of a compiler using the SUIF compiler system, which accepts programs written in Fortran77 with directives to the compiler as comments. The output of the compiler is an SPMD C program, with embedded PVM calls for message communication between the processors. We have also proposed algorithms to improve data communications,and minimizing memory usage in the output code. A uniform method for performing owner processor computations and global-to-local transformations has also been implemented.

CDAC / The introduction of languages like High Performance Fortran (HPF) which allow the programmer to indicate how the arrays used in the program have to be distributed across the local memories of a multi-computer has not completely unburdened the parallel programmer from the intricacies of these architectures. In order to tap the full potential of these architectures, the compiler has to perform this crucial task of data partitioning automatically. This would not only unburden the programmer but would make the programs more efficient since the compiler can be made more intelligent to take care of the architectural nuances. The topic of this thesis namely the automatic data partitioning deals with finding the best data partition for the various arrays used in the entire program in such a way that the cost of execution of the entire program is minimized. The compiler could resort to runtime redistribution of the arrays at various points in the program if found profitable. Several aspects of this problem have been proven to be NP-complete. Other researchers have suggested heuristic solutions to solve this problem. In this thesis we propose a genetic algorithm namely the Hierarchical Genetic Search algorithm to solve this problem.