Browse Theses

There are three fundamental problems to be solved in the execution of a parallel program on a multiprocessor--identifying the parallelism in the program, partitioning the program into tasks and scheduling the tasks on processors. Whereas the problem of identifying parallelism is a programming language issue, the partitioning and scheduling problems are intimately related to parameters of the target multiprocessor, like the number of processors and synchronisation and communication overhead. It is desirable for the partitioning and scheduling to be performed automatically, so that the same parallel program can execute efficiently on different multiprocessors. This dissertation presents two solutions to the partitioning and scheduling problems. The first approach is based on a macro-dataflow model, where the program is partitioned into tasks at compile-time and the tasks are scheduled on processors at run-time. The second approach is based on a compile-time scheduling model, where the partitioning of the program and the scheduling of tasks on processors are both performed at compile-time.Both approaches have been implemented to partition programs written in the single-assignment language, SISAL. The inputs to the partitioning and scheduling algorithms are a graphical representation of the program and a list of parameters describing the target multiprocessor. Execution profile information is used to derive compile-time estimates of execution times and data sizes in the program. Both the macro-dataflow and compile-time scheduling problems are expressed as optimisation problems, which are proved to be NP-complete in the strong sense. This dissertation presents efficient approximation algorithms for these problems. The effectiveness of the partitioning and scheduling algorithms is studied by multiprocessor simulations of various SISAL benchmark programs for different target multiprocessor parameters.