A unified programming system for a multi-paradigm parallel architecture

Abstract

Real time image understanding and image generation require very large amounts of computing power. A possible way to meet these requirements is to make use of the power available from parallel computing systems. However parallel machines exhibit performance which is highly dependent on the algorithms being executed. Both image understanding and image generation involve the use of a wide variety of algorithms. A parallel machine suited to some of these algorithms may be unsuited to others. This thesis describes a novel heterogeneous parallel architecture optimised for image based applications. It achieves its performance by combining two different forms of parallel architecture, namely fine grain SIMD and course grain MIMD, into a single architecture. In this way it is possible to match the most appropriate computing resource to each algorithm in a given application. As important as the architecture itself is a method for programming it. This thesis describes a novel multi-paradigm programming language based on C++, which allows programs which make use of both control and data parallelism to be expressed in a single coherent framework, based on object oriented programming. To demonstrate the utility of both the architecture and the programming system, two applications, one from the field of image understanding the other image generation are examined. These applications combine some novel algorithms with other novel implementation approaches to provide the most effective mapping onto this architecture