Running parallel applications on a heterogeneous environment with accessible development practices and automatic scalability

Abstract

Grid computing makes it possible to gather large quantities of resources to work on a problem. In order to exploit this potential, a framework that presents the resources to the user programmer in a form that maintains productivity is necessary. The framework must not only provide accessible development, but it must make efficient use of the resources. The Seeds framework is proposed. It uses the current Grid and distributed computing middleware to provide a parallel programming environment to a wider community of programmers. The framework was used to investigate the feasibility of scaling skeleton/pattern parallel programming into Grid computing. The research accomplished two goals: it made parallel programming on the Grid more accessible to domain­specific programmers, and it made parallel programs scale on a heterogeneous resource environ­ ment. Programming is made easier to the programmer by using skeleton and pat­ tern­based programming approaches that effectively isolate the program from the envi­ ronment. To extend the pattern approach, the pattern adder operator is proposed, imple­ mented and tested. The results show the pattern operator can reduce the number of lines of code when compared with an MPJ­Express implementation for a stencil algorithm while having an overhead of at most ten microseconds per iteration. The research in scal­ ability involved adapting existing load­balancing techniques to skeletons and patterns re­ quiring little additional configuration on the part of the programmer. The hierarchical de­ pendency concept is proposed as well, which uses a streamed data flow programming model. The concept introduces data flow computation hibernation and dependencies that can split to accommodate additional processors. The results from implementing skeleton/patterns on hierarchical dependencies show an 18.23% increase in code is neces­ sary to enable automatic scalability. The concept can increase speedup depending on the algorithm and grain size