GMRES: a generalized minimal residual algorithm for solving nonsymmetric linear systems
SIAM Journal on Scientific and Statistical Computing
Characterizing the behavior of sparse algorithms on caches
Proceedings of the 1992 ACM/IEEE conference on Supercomputing
Improving the memory-system performance of sparse-matrix vector multiplication
IBM Journal of Research and Development
On Improving the Performance of Sparse Matrix-Vector Multiplication
HIPC '97 Proceedings of the Fourth International Conference on High-Performance Computing
Enabling high-fidelity neutron transport simulations on petascale architectures
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
Loop transformation recipes for code generation and auto-tuning
LCPC'09 Proceedings of the 22nd international conference on Languages and Compilers for Parallel Computing
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Cumulative reaction probability (CRP) calculations providea viable computational approach to estimate reaction rate coefficients.However, in order to give meaningful results these calculations shouldbe done in many dimensions (ten to fifteen). This makes CRP codesmemory intensive. For this reason, these codes use iterative methods tosolve the linear systems, where a good fraction of the execution timeis spent on matrix-vector multiplication. In this paper, we discuss thetensor product form of applying the system operator on a vector. Thisapproach shows much better performance and provides huge savings inmemory as compared to the explicit sparse representation of the systemmatrix.