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High-Performance Parallel and Distributed Computing for the BMI Eigenvalue Problem

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High-Performance Parallel and Distributed Computing for the BMI Eigenvalue Problem
The BMI Eigenvalue Problem is one of optimization problems and is to minimize the greatest eigenvalue of a bilinear matrix function. This paper proposes a parallel algorithm to compute the ¯-optimal solution of the BMI Eigenvalue Problem on parallel and distributed computing systems. The proposed algorithm performs a parallel branch and bound method to compute the ¯-optimal solution using the Master-Worker paradigm. The performance evaluation results on PC clusters and a Grid computing system showed that the proposed algorithm reduced computation time of the BMI Eigenvalue problem to 1/91 of the sequential computation time on a PC cluster with 128CPUs and reduced that to 1/7 on a Grid computing system. The results also showed that tuning of the computational granularity on a worker was required to achieve the best performance on a Grid computing system.
Kento Aida, Yoshiaki Futakata, Shinji Hara
Added 15 Jul 2010
Updated 15 Jul 2010
Type Conference
Year 2002
Where IPPS
Authors Kento Aida, Yoshiaki Futakata, Shinji Hara
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