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Graphical Processing Units for Quantum Chemistry
Abstract
Graphical processing units (GPUs) are rapidly outpacing conventional CPUs in computational performance. The authors provide a brief overview of electronic structure theory and detail their experiences implementing quantum chemistry methods on the GPU, demonstrating speedups of up to 93x for direct self-consistent field calculations on a variety of molecules. They analyze the performance of the algorithms in terms of floating-point operations and memory bandwidth. They also assess the adequacy of the single-precision accuracy for quantum chemistry applications.
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Citations
Cited by
Ufimtsev, I. S., & Martinez, T. J. (2009) · Journal of Chemical Theory and Computation · doi:10.1021/ct9003004 · Cited by 916
Self-citationQuantum Chemistry on Graphical Processing Units. 2. Direct Self-Consistent-Field Implementation
Ufimtsev, I. S., & Martinez, T. J. (2009) · Journal of Chemical Theory and Computation · doi:10.1021/ct800526s · Cited by 451
Self-citationBlum, V., Asahi, R., Autschbach, J., Bannwarth, C., Bihlmayer, G., Blügel, S., Burns, L. A., Crawford, T. D., Dawson, W., de Jong, W. A., et al. (2024) · Electronic Structure · doi:10.1088/2516-1075/ad48ec · Cited by 24
High-performance dynamic quantum clustering on graphics processors
Wittek, P. (2013) · Journal of Computational Physics · doi:10.1016/j.jcp.2012.08.048 · Cited by 16
Metadata
- Authors
- Ufimtsev, I. S., & Martínez, T. J.
- Venue
- Computing in Science & Engineering
- Published
- 2008-11
- Volume
- 10
- Issue
- 6
- Pages
- 26-34
- ISSN
- 1521-9615 (ISSN-L)
- OpenAlex
- W1998484176
- Cited by
- 198
- References
- 0
- FWCI
- 21.39
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ufimtsev, I. S., & Martínez, T. J. (2008). Graphical Processing Units for Quantum Chemistry. Computing in Science & Engineering. https://doi.org/10.1109/mcse.2008.148
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.