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Improving the Performance of Tensor Matrix Vector Multiplication in Quantum Chemistry Codes
| Content Provider | Semantic Scholar |
|---|---|
| Author | Gropp, William D. Kaushik, Dinesh K. Minkoff, Michael Smith, Barry F. |
| Copyright Year | 2005 |
| Abstract | 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. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://wgropp.cs.illinois.edu/bib/papers/pdata/2005/kaushik-tensor.pdf |
| Alternate Webpage(s) | http://www-unix.mcs.anl.gov/~gropp/bib/papers/2005/kaushik-tensor.pdf |
| Alternate Webpage(s) | http://www.mcs.anl.gov/~gropp/bib/papers/2005/kaushik-tensor.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Blocking (computing) CPU cache Code Coefficient Computation Iterative method Linear system Matrix multiplication Organic Chemistry Phenomena Quantum Run time (program lifecycle phase) Sparse approximation Sparse matrix Sysop |
| Content Type | Text |
| Resource Type | Article |
