Agent and Spatial Based Parallelization of Biological Network Motif Search

Most graph algorithms are challenging in parallelization, in particular executing fine-grain computation at each graph node in parallel from both programmability and performance viewpoints. To bridge the semantic gap between the original sequential algorithms and their corresponding parallelized programs, we have been developing MASS: a parallel library for multi-agent spatial simulation. The library allows software agents to crawl a distributed array, e.g., a graph mapped over a cluster system. To demonstrate the MASS library's fitness to graph parallelization, we have focused on biological network motif search. This paper compares three different parallelizing approaches such as the MASS agent-based, MASS array-based, and the conventional MPI parallelizations, and discusses the MASS library's applicability to graph algorithms.