Improving the performance of tensor matrix vector multiplication in quantum chemistry codes [electronic resource].
- Washington, D.C. : United States. Dept. of Energy. Office of Science, 2008. and Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
- Additional Creators:
- Argonne National Laboratory, United States. Department of Energy. Office of Science, and United States. Department of Energy. Office of Scientific and Technical Information
- Restrictions on Access:
- Free-to-read Unrestricted online access
- Cumulative reaction probability (CRP) calculations provide a viable computational approach to estimate reaction rate coefficients. However, in order to give meaningful results these calculations should be done in many dimensions (ten to fifteen). This makes CRP codes memory intensive. For this reason, these codes use iterative methods to solve the linear systems, where a good fraction of the execution time is spent on matrix-vector multiplication. In this paper, we discuss the tensor product form of applying the system operator on a vector. This approach shows much better performance and provides huge savings in memory as compared to the explicit sparse representation of the system matrix.
- Published through SciTech Connect., 05/08/2008., "anl/mcs-tm-297", and Minkoff, M.; Gropp, W. D.; Kaushik, D. K.; Smith, B. F.
- Funding Information:
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