Publications
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Filters: Author is Hartwig Anzt [Clear All Filters]
On the performance and energy efficiency of sparse linear algebra on GPUs,”
International Journal of High Performance Computing Applications, October 2016.
DOI: 10.1177/1094342016672081 (1.19 MB)
“GPU-Accelerated Asynchronous Error Correction for Mixed Precision Iterative Refinement,”
University of Tennessee Computer Science Technical Report UT-CS-11-690 (also Lawn 260), December 2011.
(662.98 KB)
“MAGMA-sparse Interface Design Whitepaper,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-17-05, September 2017.
(1.28 MB)
“Implementing a Sparse Matrix Vector Product for the SELL-C/SELL-C-σ formats on NVIDIA GPUs,”
University of Tennessee Computer Science Technical Report, no. UT-EECS-14-727: University of Tennessee, April 2014.
(578.11 KB)
“Variable-Size Batched Condition Number Calculation on GPUs,”
SBAC-PAD, Lyon, France, September 2018.
(509.3 KB)
“Ginkgo: A High Performance Numerical Linear Algebra Library,”
Journal of Open Source Software, vol. 5, issue 52, August 2020.
DOI: 10.21105/joss.02260 (721.84 KB)
“Accelerating the LOBPCG method on GPUs using a blocked Sparse Matrix Vector Product,”
Spring Simulation Multi-Conference 2015 (SpringSim'15), Alexandria, VA, SCS, April 2015.
(1.46 MB)
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