Optimization and enhancement strategies for data flow systems

The data flow machine, which represents a radical departure from the conventional von Neumann architecture, shows great potential as a candidate for the future generation of computers. The difficulty in the usage of data structures as well as the effective exploitation of parallelism are two issues which have not as yet been fully resolved within the framework of the data flow model.This thesis concentrates on these important problems in the following manner. Firstly, the role memory can play in a data flow system is examined. A new concept called "active memory" is introduced together with various new actors. It is shown that these enhancements make it possible to implement a limited form of shared memory which readily supports the use of data structures.Secondly, execution performance of data flow programs is examined in the context of conditional statements. Transformations applied to the data flow graph are presented which increase the degree of parallelism. Analysis, both theoretical and empirical, is performed, showing that substantial improvements are obtained with a minimal impact on other system components