Distributed Computing with Hierarchical Master-worker Paradigm for Parallel Branch and Bound Algorithm

This paper discusses the impact of the hierarchical master-worker paradigm on performance of an application program, which solves an optimization problem by a parallel branch and bound algorithm on a distributed computing system. The application program, which this paper addresses, solves the BMI Eigenvalue Problem, which is an optimization problem to minimize the greatest eigenvalue of a bilinear matrix function. This paper proposes a parallel branch and bound algorithm to solve the BMI Eigenvalue Problem with the hierarchical master-worker paradigm. The experimental results showed that the conventional algorithm with the master-worker paradigm significantly degraded performance on a Grid test bed, where computing resources were distributed on WAN via a firewall; however, the hierarchical master-worker paradigm sustained good performance.

• Type: Conference
• Year: 2003
• Where: CCGRID
• Authors: Kento Aida, Wataru Natsume, Yoshiaki Futakata
• Keywords: BMI Eigenvalue Problem, CCGRID 2003, Cluster Computing, Hierarchical Master-worker Paradigm, Optimization Problem