PhD ThesisIn the 1980s, multiple-processor computers (multiprocessors) based on conven-
tional processing elements emerged as a popular solution to the continuing demand
for ever-greater computing power. These machines offer a general-purpose parallel
processing platform on which the size of program units which can be efficiently
executed in parallel - the "grain size" - is smaller than that offered by distributed
computing environments, though greater than that of some more specialised
architectures. However, programming to exploit this medium-grained parallelism
remains difficult. Concurrent execution is inherently complex, yet there is a lack of
programming tools to support parallel programming activities such as program
design, implementation, debugging, performance tuning and so on.
In helping to manage complexity in sequential programming, visual tools have
often been used to great effect, which suggests one approach towards the goal of
making parallel programming less difficult.
This thesis examines the possibilities which the dataflow paradigm has to offer
as the basis for a set of visual parallel programming tools, and presents a dataflow
notation designed as a framework for medium-grained parallel programming. The
implementation of this notation as a programming language is discussed, and its
suitability for the medium-grained level is examinedScience and Engineering Research Council of Great Britain
EC ERASMUS schem
