Publications
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Filters: Author is Jakub Kurzak [Clear All Filters]
Dense Linear Algebra on Accelerated Multicore Hardware,”
High Performance Scientific Computing: Algorithms and Applications, London, UK, Springer-Verlag, 00 2012.
“ A Data Flow Divide and Conquer Algorithm for Multicore Architecture,”
29th IEEE International Parallel & Distributed Processing Symposium (IPDPS), Hyderabad, India, IEEE, May 2015.
(535.44 KB)
“A Class of Parallel Tiled Linear Algebra Algorithms for Multicore Architectures,”
Parallel Computing (to appear), 00 2010.
(612.23 KB)
“A Class of Parallel Tiled Linear Algebra Algorithms for Multicore Architectures,”
Parallel Computing, vol. 35, pp. 38-53, 00 2009.
(274.74 KB)
“A Class of Parallel Tiled Linear Algebra Algorithms for Multicore Architectures,”
University of Tennessee Computer Science Technical Report, no. UT-CS-07-600 (also LAPACK Working Note 191), January 2007.
(274.74 KB)
“Changes in Dense Linear Algebra Kernels - Decades Long Perspective,”
in Solving the Schrodinger Equation: Has everything been tried? (to appear): Imperial College Press, 00 2011.
“The Case for Directive Programming for Accelerator Autotuner Optimization,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-17-07: University of Tennessee, October 2017.
(341.52 KB)
“C++ API for BLAS and LAPACK,”
SLATE Working Notes, no. 02, ICL-UT-17-03: Innovative Computing Laboratory, University of Tennessee, June 2017.
(1.12 MB)
“C++ API for Batch BLAS,”
SLATE Working Notes, no. 04, ICL-UT-17-12: University of Tennessee, December 2017.
(1.89 MB)
“Bringing High Performance Computing to Big Data Algorithms,”
Handbook of Big Data Technologies: Springer, 2017.
DOI: 10.1007/978-3-319-49340-4 (1.22 MB)
“Autotuning Techniques for Performance-Portable Point Set Registration in 3D,”
Supercomputing Frontiers and Innovations, vol. 5, no. 4, December 2018.
DOI: 10.14529/jsfi180404 (720.15 KB)
“Autotuning Numerical Dense Linear Algebra for Batched Computation With GPU Hardware Accelerators,”
Proceedings of the IEEE, vol. 106, issue 11, pp. 2040–2055, November 2018.
DOI: 10.1109/JPROC.2018.2868961 (2.53 MB)
“Autotuning GEMMs for Fermi,”
University of Tennessee Computer Science Technical Report, UT-CS-11-671, (also Lawn 245), April 2011.
(397.45 KB)
“Autotuning GEMM Kernels for the Fermi GPU,”
IEEE Transactions on Parallel and Distributed Systems, vol. 23, no. 11, November 2012.
DOI: 10.1109/TPDS.2011.311 (742.5 KB)
“Autotuning Batch Cholesky Factorization in CUDA with Interleaved Layout of Matrices,”
Parallel and Distributed Processing Symposium Workshops (IPDPSW), Orlando, FL, IEEE, June 2017.
DOI: 10.1109/IPDPSW.2017.18
“On Algorithmic Variants of Parallel Gaussian Elimination: Comparison of Implementations in Terms of Performance and Numerical Properties,”
University of Tennessee Computer Science Technical Report, no. UT-CS-13-715, July 2013, 2012.
(358.98 KB)
“Access-averse Framework for Computing Low-rank Matrix Approximations,”
First International Workshop on High Performance Big Graph Data Management, Analysis, and Mining, Washington, DC, October 2014.
“Accelerating Scientific Computations with Mixed Precision Algorithms,”
Computer Physics Communications, vol. 180, issue 12, pp. 2526-2533, December 2009.
DOI: 10.1016/j.cpc.2008.11.005 (402.69 KB)
“Accelerating Numerical Dense Linear Algebra Calculations with GPUs,”
Numerical Computations with GPUs: Springer International Publishing, pp. 3-28, 2014.
DOI: 10.1007/978-3-319-06548-9_1 (1.06 MB)
“Accelerating Collaborative Filtering for Implicit Feedback Datasets using GPUs,”
2015 IEEE International Conference on Big Data (IEEE BigData 2015), Santa Clara, CA, IEEE, November 2015.
(1.02 MB)
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