Computation of Pseudospectra by Continuation
SIAM Journal on Scientific Computing
Parallel Computation of Spectral Portraits on the Meiko CS2
HPCN Europe 1996 Proceedings of the International Conference and Exhibition on High-Performance Computing and Networking
Parallel Computation of Pseudospectra Using Transfer Functions on a MATLAB-MPI Cluster Platform
Proceedings of the 9th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface
The design of a distributed MATLAB-based environment for computing pseudospectra
Future Generation Computer Systems - Special section: Complex problem-solving environments for grid computing
Spatial aggregation for qualitative assessment of scientific computations
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
The design of a distributed MATLAB-based environment for computing pseudospectra
Future Generation Computer Systems - Special section: Complex problem-solving environments for grid computing
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Given a matrix A, the computation of its pseudospectrum A∈ (A) is a far more expensive task than the computation of characteristics such as the condition number and the matrix spectrum. As research of the last 15 years has shown, however, the matrix pseudospectrum provides valuable information that is not included in other indicators. So, we ask how to compute it efficiently and build a tool that would facilitate engineers and scientists to make such analyses? In this paper we focus on parallel algorithms for computing pseudospectra. The most widely used algorithm for computing pseudospectra is embarassingly parallel; nevertheless, it is extremely costly and one cannot hope achieve absolute high performance with it. We describe algorithms that have drastically improved performance while maintaining a high degree of large grain parallelism. We evaluate the effectiveness of these methods in the context of a MATLAB-based environment for parallel programming using MPI on small, off-the-shelf parallel systems.