Extension by Refining Task Granularity for Parallel Computation with Variable Structures

Abstract:

Aiming at such extension problem in parallel computation, this paper evaluates the key factors from parallel tasks and architecture which affect the scalability, and then models parallel tasks as well as architecture by the weighted graph. Especially, we propose the extension method of refining task granularity to realize an extension in parallel computation. The extension method transforms the graph’s structure and adjusts the weights of its nodes and edges in essence. Additionally, by further derivation, some significant conclusions about the new extension methods are drawn. Finally, the simulative experiments are conducted on the platform SimGrid to verify the effectiveness of the proposed extension methods. The results show that the new methods can realize isospeede extension in parallel computation with variable structures, which is helpful for its practical extension.