Numerical Linear Algebra for High Performance Computers
Numerical Linear Algebra for High Performance Computers
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Solving Sparse Linear Systems on NVIDIA Tesla GPUs
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
The iterative solver template library
PARA'06 Proceedings of the 8th international conference on Applied parallel computing: state of the art in scientific computing
Concurrent number cruncher: an efficient sparse linear solver on the GPU
HPCC'07 Proceedings of the Third international conference on High Performance Computing and Communications
Parallel GMRES implementation for solving sparse linear systems on GPU clusters
Proceedings of the 19th High Performance Computing Symposia
Hi-index | 0.00 |
Scientific applications very often rely on solving one or more linear systems. When matrices are sparse, iterative methods are preferred to direct ones. Nevertheless, the value of nonzero elements and their distribution (i.e., the sketch of the matrix) greatly influence the efficiency of those methods (in terms of computation time, number of iterations, result precision) or simply prevent the convergence.Among iterative methods, GMRES (Saad, Iterative methods for sparse linear systems. PWS Publishing, New York, 1996) is often chosen when dealing with general nonsymmetric matrices. Indeed its convergence is very fast and more stable than the biconjugate gradient. Furthermore, it is mainly based on mathematical operations (matrix-vector and dot products, norms, etc.) that can be heavily parallelized and is thus a good candidate to implement a solver for sparse systems on Graphics Processing Units (GPU).This paper presents a GMRES method for such an architecture. It is based on the modified Gram---Schmidt approach and is very similar to that of Sparselib (Barrett et al., Templates for the solution of linear systems: building blocks for iterative methods, SIAM, Philadelphia, 1994). Our version uses restarting and a very basic preconditioning. For its implementation, we have based our code on CUBLAS (NVIDIA, http://developer.download.nvidia.com/compute/cuda/2_1/toolkit/docs/CUBLAS_Library_2.1.pdf , 2008) and SpMV (Bell and Garland, Efficient sparse matrix-vector multiplication on CUDA. NVIDIA technical report NVR-2008-004, 2008) libraries, in order to achieve a good performance whatever the matrix sizes and their sketch are. Our experiments exhibit encouraging results on the comparison between Central Processing Units (CPU) and GPU executions in double precision, obtaining a speedup ranging from 8 up to 23 for a large variety of problems.