Scalable Parallel Graph-Based Algorithms for Complex Network Simulations

Abstract

Simulations on large-scale complex networks underpin the study of systems within social interactions, biological processes, and infrastructure networks. Nevertheless, traditional graph algorithms are frequently difficult to operate at scale, as they need greater amounts of data, have irregular graph connectivity, and in parallel settings, encounter significant communication costs. The paper discusses these issues through the development of a scalable parallel graph based algorithm that can be used to efficiently simulate complex networks. The approach takes advantage of distributed and hybrid parallel computing systems, which combine message passing (MPI) with shared-memory algorithms (OpenMP/GPU acceleration) to balance computation and communication. They use a partitioning strategy to evenly distribute workload and reduce inter-processor communication and an adaptive load balancing mechanism to better utilize the resources. Experimental analysis on both large synthetic and real-world systems shows that the performance is vastly improved as compared to those of existing methods. The algorithm suggested demonstrates significant speedup as the number of processors increase, high parallel performance and has good and bad scalability with respect to the size of the graph. Optimized data exchange strategies aid in minimizing communication overhead, which contributes to the overall system performance. The findings bring out the efficiency of the suggested method within the large scale graph processing and hence it suits real-time and high-performance simulations of networks. The contribution of this work to the development of scalable graph processing methods is that it offers a foundation to further studies in parallel and distributed complex network analysis.