Analysis of a coordination framework for mapping coarse-grain applications to distributed systems

A paradigm is presented for the parallelization of coarse-grain engineering and scientific applications. The coordination framework provides structure and an organizational strategy for a parallel solution in a distributed environment. Three categories of primitives which define the coordination framework are presented: structural, transformational. and operational. The prototype of the paradigm presented in this thesis is the first step towards a programming development tool. This tool will allow non-specialist programmers to parallelize existing sequential solutions through the distribution, synchronization and collection of tasks. The distributed control, multidimensional pipeline characteristics of the paradigm provide advantages which include load balancing through the use of self-directed workers, a simplified communication scheme ideally suited for infrequent task interaction, a simple programmer interface, and the ability of the programmer to use already existing code. Results for the parallelization of SPICE3Cl in a distributed system of fifteen SUN 3 workstations with one fileserver demonstrate linear speedup with slopes ranging from 0.7 to 0.9. A high-level abstraction of the system is presented in the form of a closed, single class, queuing network model. Using the Mean Value Analysis solution technique from queuing network theory, an expression for total execution time is obtained and is shown to be consistent with the well known Amdahl\u27s Law. Our expression is in fact a refinement of Amdahl\u27s Law which realistically captures the limitations of the system. We show that the portion of time spent executing serial code which cannot be enhanced by parallelization is a function of N, the number of workers in the system. Experiments reveal the critical nature of the communication scheme and the synchronization of the paradigm. Investigation of the synchronization center indicates that as N increases, visitations to the center increase and degrade system performance. Experimental data provides the information needed to characterize the impact of visitations on the performance of the system. This characterization provides a mechanism for optimizing the speedup of an application. It is shown that the model replicates the system as well as predicts speedup over an extended range of processors, task count, and task size