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pro vyhledávání: '"Loe, Jennifer"'
Autor:
Loe, Jennifer A., Glusa, Christian A., Yamazaki, Ichitaro, Boman, Erik G., Rajamanickam, Sivasankaran
Support for lower precision computation is becoming more common in accelerator hardware due to lower power usage, reduced data movement and increased computational performance. However, computational science and engineering (CSE) problems require dou
Externí odkaz:
http://arxiv.org/abs/2109.01232
Autor:
Loe, Jennifer A., Glusa, Christian A., Yamazaki, Ichitaro, Boman, Erik G., Rajamanickam, Sivasankaran
Support for lower precision computation is becoming more common in accelerator hardware due to lower power usage, reduced data movement and increased computational performance. However, computational science and engineering (CSE) problems require dou
Externí odkaz:
http://arxiv.org/abs/2105.07544
Autor:
Abdelfattah, Ahmad, Anzt, Hartwig, Boman, Erik G., Carson, Erin, Cojean, Terry, Dongarra, Jack, Gates, Mark, Grützmacher, Thomas, Higham, Nicholas J., Li, Sherry, Lindquist, Neil, Liu, Yang, Loe, Jennifer, Luszczek, Piotr, Nayak, Pratik, Pranesh, Sri, Rajamanickam, Siva, Ribizel, Tobias, Smith, Barry, Swirydowicz, Kasia, Thomas, Stephen, Tomov, Stanimire, Tsai, Yaohung M., Yamazaki, Ichitaro, Yang, Urike Meier
Within the past years, hardware vendors have started designing low precision special function units in response to the demand of the Machine Learning community and their demand for high compute power in low precision formats. Also the server-line pro
Externí odkaz:
http://arxiv.org/abs/2007.06674
Autor:
Loe, Jennifer A., Morgan, Ronald B.
We present a polynomial preconditioner for solving large systems of linear equations. The polynomial is derived from the minimum residual polynomial (the GMRES polynomial) and is more straightforward to compute and implement than many previous polyno
Externí odkaz:
http://arxiv.org/abs/1911.07065
Polynomial preconditioning with the GMRES minimal residual polynomial has the potential to greatly reduce orthogonalization costs, making it useful for communication reduction. We implement polynomial preconditioning in the Belos package from Trilino
Externí odkaz:
http://arxiv.org/abs/1907.00072
Polynomial preconditioning can improve the convergence of the Arnoldi method for computing eigenvalues. Such preconditioning significantly reduces the cost of orthogonalization; for difficult problems, it can also reduce the number of matrix-vector p
Externí odkaz:
http://arxiv.org/abs/1806.08020
Publikováno v:
Discussiones Mathematicae Graph Theory, Vol 35, Iss 1, Pp 55-72 (2015)
A variation of graph coloring known as a t-tone k-coloring assigns a set of t colors to each vertex of a graph from the set {1, . . . , k}, where the sets of colors assigned to any two vertices distance d apart share fewer than d colors in common. Th
Externí odkaz:
https://doaj.org/article/a4f267251bf347d19f3536cea62ae9c9
Autor:
Macdiarmid, Jennie I, Kyle, Janet, Horgan, Graham W, Loe, Jennifer, Fyfe, Claire, Johnstone, Alexandra, McNeill, Geraldine
Publikováno v:
In The American Journal of Clinical Nutrition September 2012 96(3):632-639
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Akademický článek
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