Hybrid procedures for solving linear equations
Numerische Mathematik
Deploying fault tolerance and taks migration with NetSolve
Future Generation Computer Systems - Special issue on metacomputing
Templates for the solution of algebraic eigenvalue problems: a practical guide
Templates for the solution of algebraic eigenvalue problems: a practical guide
NetSolve: a network server for solving computational science problems
Supercomputing '96 Proceedings of the 1996 ACM/IEEE conference on Supercomputing
Applying NetSolve's Network-Enabled Server
IEEE Computational Science & Engineering
IMPLICITLY RESTARTED ARNOLDI/LANCZOS METHODS FOR LARGE SCALE EIGENVALUE CALCULATIONS
IMPLICITLY RESTARTED ARNOLDI/LANCZOS METHODS FOR LARGE SCALE EIGENVALUE CALCULATIONS
NetSolve version 1.2: Design and Implementation
NetSolve version 1.2: Design and Implementation
A Test Matrix Collection for Non-Hermitian Eigenvalue Problems
A Test Matrix Collection for Non-Hermitian Eigenvalue Problems
Multiple Explicitly Restarted Arnoldi Method for Solving Large Eigenproblems
SIAM Journal on Scientific Computing
Future Generation Computer Systems
Resolution of large symmetric eigenproblems on a world-wide grid
International Journal of Grid and Utility Computing
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The explicitly restarted Arnoldi method (ERAM) allows one to find a few eigenpairs of a large sparse matrix. The multiple explicitly restarted Arnoldi method (MERAM) is a technique based upon a multiple projection of ERAM and accelerates its convergence [N. Emamad, S. Petiton, G. Edjlali, Multiple explicitly restarted Arnoldi method for solving large eigenproblems. SIAM J. Sci. Comput. SJSC 27 (1) (2005) 253-277]. MERAM allows one to update the restarting vector of an ERAM by taking into account the interesting eigen-information obtained by its other ERAM processes. This method is particularly well suited to the GRID-type environments. We present an adaptation of the asynchronous version of MERAM for the NetSolve global computing system. We point out some advantages and limitations of this kind of system to implement the asynchronous hybrid algorithms. We give some results of our experiments and show that we can obtain a good acceleration of the convergence compared to ERAM. These results also show the potential of the MERAM-like hybrid methods for the GRID computing environments.