Direct methods for sparse matrices
Direct methods for sparse matrices
Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Multisplitting of a symmetric positive definite matrix
SIAM Journal on Matrix Analysis and Applications
s-step iterative methods for symmetric linear systems
Journal of Computational and Applied Mathematics
s-step iterative methods for (non)symmetric (in)definite linear systems
SIAM Journal on Numerical Analysis
Matrix computations (3rd ed.)
Parallel Preconditioning with Sparse Approximate Inverses
SIAM Journal on Scientific Computing
A Supernodal Approach to Sparse Partial Pivoting
SIAM Journal on Matrix Analysis and Applications
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
A Decentralized Convergence Detection Algorithm for Asynchronous Parallel Iterative Algorithms
IEEE Transactions on Parallel and Distributed Systems
Parallelization of Direct Algorithms using Multisplitting Methods in Grid Environments
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 13 - Volume 14
Hybrid scheduling for the parallel solution of linear systems
Parallel Computing - Parallel matrix algorithms and applications (PMAA'04)
GStokes: A Grid-enabled Solver for the 3D Stokes/Navier-Stokes System on Hybrid Meshes
ISPDC '07 Proceedings of the Sixth International Symposium on Parallel and Distributed Computing
Grid'5000: A Large Scale And Highly Reconfigurable Experimental Grid Testbed
International Journal of High Performance Computing Applications
GREMLINS: a large sparse linear solver for grid environment
Parallel Computing
Large Scale Parallel Hybrid GMRES Method for the Linear System on Grid System
ISPDC '08 Proceedings of the 2008 International Symposium on Parallel and Distributed Computing
PSPIKE: A Parallel Hybrid Sparse Linear System Solver
Euro-Par '09 Proceedings of the 15th International Euro-Par Conference on Parallel Processing
On the performance of parallel normalized explicit preconditioned conjugate gradient type methods
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
| Hi-index | 0.00 |
Solving large sparse linear systems is essential in numerous scientific domains. Several algorithms, based on direct or iterative methods, have been developed for parallel architectures. On distributed grids consisting of processors located in distant geographical sites, their performance may be unsatisfactory because they suffer from too many synchronizations and communications. The GREMLINS code has been developed for solving large sparse linear systems on distributed grids. It implements the multisplitting method that consists in splitting the original linear system into several subsystems that can be solved independently. In this paper, the performance of the GREMLINS code obtained with several libraries for solving the linear subsystems is analyzed. Its performance is also compared with that of the widely used PETSc library that enables one to develop portable parallel applications. Numerical experiments have been carried out both on local clusters and on distributed grids.