Block algorithms for sparse matrix computations on high performance workstations
ICS '96 Proceedings of the 10th international conference on Supercomputing
An Approximate Minimum Degree Ordering Algorithm
SIAM Journal on Matrix Analysis and Applications
Improving the memory-system performance of sparse-matrix vector multiplication
IBM Journal of Research and Development
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Improving performance of sparse matrix-vector multiplication
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
Modeling data locality for the sparse matrix-vector product using distance measures
Parallel Computing - Linear systems and associated problems
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Sparse Tiling for Stationary Iterative Methods
International Journal of High Performance Computing Applications
Sparsity: Optimization Framework for Sparse Matrix Kernels
International Journal of High Performance Computing Applications
Performance Optimization and Modeling of Blocked Sparse Kernels
International Journal of High Performance Computing Applications
Optimization of sparse matrix-vector multiplication on emerging multicore platforms
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Optimizing sparse matrix-vector multiplication using index and value compression
Proceedings of the 5th conference on Computing frontiers
The Traveling Salesman Problem: A Computational Study (Princeton Series in Applied Mathematics)
The Traveling Salesman Problem: A Computational Study (Princeton Series in Applied Mathematics)
Reordering Algorithms for Increasing Locality on Multicore Processors
HPCC '08 Proceedings of the 2008 10th IEEE International Conference on High Performance Computing and Communications
Fast sparse matrix-vector multiplication by exploiting variable block structure
HPCC'05 Proceedings of the First international conference on High Performance Computing and Communications
| Hi-index | 0.00 |
Irregular codes are present in many scientific applications, such as finite element simulations. In these simulations the solution of large sparse linear equation systems is required, which are often solved using iterative methods. The main kernel of the iterative methods is the sparse matrixâ聙聰vector multiplication which frequently demands irregular data accesses. Therefore, techniques that increase the performance of this operation will have a great impact on the global performance of the iterative method and, as a consequence, on the simulations. In this paper a technique for improving the locality of sparse matrix codes is presented. The technique consists of reorganizing the data guided by a locality model instead of restructuring the code or changing the sparse matrix storage format. We have applied our proposal to different iterative methods provided by two standard numerical libraries. Results show an impact on the overall performance of the considered iterative method due to the increase in the locality of the sparse matrixâ聙聰vector product. Noticeable reductions in the execution time have been achieved both in sequential and in parallel executions. This positive behavior allows the reordering technique to be successfully applied to real problems. We have focused on the simulation of semiconductor devices and in particular on the BIPS3D simulator. The technique was integrated into the simulator. Both sequential and parallel executions have been analyzed extensively in this paper. Noticeable reductions in the execution time required by the simulations are observed when using our reordered matrices in comparison with the original simulator.