Browse Theses

Functional languages have recently gained attention as vehicles for programming in a concise and elegant manner. In addition, it has been suggested that functional programming provides a natural methodology for programming multiprocessor computers. This dissertation demonstrates that multiprocessor execution of functional programs is feasible, and results in a significant reduction in their execution times.

Two implementations of the functional language ALFL were built on commercially available multiprocessors. Alfalfa is an implementation on the Intel iPSC hypercube multiprocessor, and Buckwheat is an implementation on the Encore Multimax shared-memory multiprocessor. Each implementation includes a compiler that performs automatic decomposition of ALFL programs. The compiler is responsible for detecting the inherent parallelism in a program, and decomposing the program into a collection of tasks, called serial combinators, that can be executed in parallel. One of the primary goals of the compiler is to generate serial combinators exhibiting the coarsest granularity possibly without sacrificing useful parallelism. This dissertation describes the algorithms used by the compiler to analyze, decompose, and optimize functional programs.

The abstract machine model supported by Alfalfa and Buckwheat is called heterogeneous graph reduction, which is a hybrid of graph reduction and conventional stack-oriented execution. This model supports parallelism, lazy evaluation, and higher order functions while at the same time making efficient use of the processors in the system. The Alfalfa and Buckwheat run-time systems support dynamic load balancing, interprocessor communication (if required), and storage management. A large number of experiments were performed on Alfalfa and Buckwheat for a variety of programs. The results of these experiments, as well as the conclusions drawn from them, are presented.