High Performance Fortran (HPF), as well as its predecessor FortranD, has attracted considerable attention as a promising language for writing portable parallel programs for a wide variety of distributed-memory architectures. Programmers express data parallelism using Fortran90 array operations and use data layout directives to direct the partitioning of the data and computation among the processors of a parallel machine. For HPF to gain acceptance as a vehicle for parallel scientific programming, it must achieve high performance on problems for which it is well suited. To achieve high performance with an HPF program on a distributed-memory parallel machine, an HPF compiler must do a superb job of translating Fortran90 data-parallel array constructs into an efficient sequence of operations that minimize the overhead associated with data movement and also maximize data locality. This dissertation presents and analyzes a set of advanced optimizations designed to improve the execution perf...

Data-parallel languages allow programmers to use the familiar machine-independent programming style to develop programs for multiprocessor systems. These languages relieve users of the tedious task of inserting interprocessor communication and delegate this crucial and error-prone task to the compilers for the languages. Since remote access in hierarchical multiprocessor systems is orders of magnitude slower than access to a processor's local memory, interprocessor communication introduces significant overheads to the total execution time. The success of data-parallel languages depends heavily on the compiler's ability to reduce the communication overhead. This dissertation describes novel techniques for communication generation. It covers issues related to communication analysis, placement, and optimization. The techniques have been implemented in the Rice Fortran D95 research compiler -- a High Performance Fortran (HPF) compiler -- being developed at the Rice University. A major cont...