Publications
“Block-asynchronous Multigrid Smoothers for GPU-accelerated Systems,”
ICCS 2012, Omaha, NE, June 2012.
(608.95 KB)
(608.95 KB)
MAGMA: A New Generation of Linear Algebra Library for GPU and Multicore Architectures
, Salt Lake City, UT, The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC12), Presentation, November 2012.
(4.69 MB)
(4.69 MB)
MAGMA MIC: Linear Algebra Library for Intel Xeon Phi Coprocessors
, Salt Lake City, UT, The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC12), November 2012.
(6.4 MB)
(6.4 MB)
MAGMA Tutorial
, Atlanta, GA, Keeneland Workshop, February 2012.
(2.47 MB)
(2.47 MB)
MAGMA MIC: Optimizing Linear Algebra for Intel Xeon Phi
, Frankfurt, Germany, ISC High Performance (ISC15), Intel Booth Presentation, June 2015.
(2.03 MB)
(2.03 MB)
“Parallel Programming Models for Dense Linear Algebra on Heterogeneous Systems,”
Supercomputing Frontiers and Innovations, vol. 2, no. 4, October 2015.
DOI: 10.14529/jsfi1504 (3.68 MB)
(3.68 MB)
“A Survey of Recent Developments in Parallel Implementations of Gaussian Elimination,”
Concurrency and Computation: Practice and Experience, vol. 27, issue 5, pp. 1292-1309, April 2015.
DOI: 10.1002/cpe.3306 (783.45 KB)
(783.45 KB)
“Heterogeneous Streaming,”
The Sixth International Workshop on Accelerators and Hybrid Exascale Systems (AsHES), IPDPS 2016, Chicago, IL, IEEE, May 2016.
(2.73 MB)
(2.73 MB)
“Linear Algebra Software for Large-Scale Accelerated Multicore Computing,”
Acta Numerica, vol. 25, pp. 1-160, May 2016.
DOI: 10.1017/S0962492916000015
“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
“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)
(1.22 MB)
“C++ API for Batch BLAS,”
SLATE Working Notes, no. 04, ICL-UT-17-12: University of Tennessee, December 2017.
(1.89 MB)
(1.89 MB)
“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)
(1.12 MB)
“Designing SLATE: Software for Linear Algebra Targeting Exascale,”
SLATE Working Notes, no. 03, ICL-UT-17-06: Innovative Computing Laboratory, University of Tennessee, October 2017.
(2.8 MB)
(2.8 MB)
“PLASMA 17 Performance Report,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-17-11: University of Tennessee, June 2017.
(7.57 MB)
(7.57 MB)
“PLASMA 17.1 Functionality Report,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-17-10: University of Tennessee, June 2017.
(1.8 MB)
(1.8 MB)
“Preconditioned Krylov Solvers on GPUs,”
Parallel Computing, June 2017.
DOI: 10.1016/j.parco.2017.05.006 (1.19 MB)
(1.19 MB)
“Roadmap for the Development of a Linear Algebra Library for Exascale Computing: SLATE: Software for Linear Algebra Targeting Exascale,”
SLATE Working Notes, no. 01, ICL-UT-17-02: Innovative Computing Laboratory, University of Tennessee, June 2017.
(2.8 MB)
(2.8 MB)
“With Extreme Computing, the Rules Have Changed,”
Computing in Science & Engineering, vol. 19, issue 3, pp. 52-62, May 2017.
DOI: 10.1109/MCSE.2017.48 (485.34 KB)
(485.34 KB)
Accelerating Linear Algebra with MAGMA
, Knoxville, TN, ECP Annual Meeting 2018, Tutorial, February 2018.
(35.27 MB)
(35.27 MB)
“Accelerating the SVD Two Stage Bidiagonal Reduction and Divide and Conquer Using GPUs,”
Parallel Computing, vol. 74, pp. 3–18, May 2018.
DOI: 10.1016/j.parco.2017.10.004 (1.34 MB)
(1.34 MB)
“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)
(2.53 MB)
Batched BLAS (Basic Linear Algebra Subprograms) 2018 Specification
, July 2018.
(483.05 KB)
(483.05 KB)
“Computational Benefit of GPU Optimization for Atmospheric Chemistry Modeling,”
Journal of Advances in Modeling Earth Systems, vol. 10, issue 8, pp. 1952–1969, August 2018.
DOI: 10.1029/2018MS001276 (3.4 MB)
(3.4 MB)
“Implementation of the C++ API for Batch BLAS,”
SLATE Working Notes, no. 07, ICL-UT-18-04: Innovative Computing Laboratory, University of Tennessee, June 2018.
(1.07 MB)
(1.07 MB)
“Least Squares Performance Report,”
SLATE Working Notes, no. 09, ICL-UT-18-10: Innovative Computing Laboratory, University of Tennessee, December 2018.
(1.76 MB)
(1.76 MB)
“Linear Systems Performance Report,”
SLATE Working Notes, no. 08, ICL-UT-18-08: Innovative Computing Laboratory, University of Tennessee, September 2018.
(1.64 MB)
(1.64 MB)
“Parallel BLAS Performance Report,”
SLATE Working Notes, no. 05, ICL-UT-18-01: University of Tennessee, April 2018.
(4.39 MB)
(4.39 MB)
“Parallel Norms Performance Report,”
SLATE Working Notes, no. 06, ICL-UT-18-06: Innovative Computing Laboratory, University of Tennessee, June 2018.
(1.13 MB)
(1.13 MB)
“The Singular Value Decomposition: Anatomy of Optimizing an Algorithm for Extreme Scale,”
SIAM Review, vol. 60, issue 4, pp. 808–865, November 2018.
DOI: 10.1137/17M1117732 (2.5 MB)
(2.5 MB)
“Least Squares Solvers for Distributed-Memory Machines with GPU Accelerators,”
ACM International Conference on Supercomputing (ICS '19), Phoenix, Arizona, ACM, pp. 117–126, June 2019.
DOI: https://dl.acm.org/doi/abs/10.1145/3330345.3330356 (1.63 MB)
(1.63 MB)
“Linear Systems Solvers for Distributed-Memory Machines with GPU Accelerators,”
Euro-Par 2019: Parallel Processing, vol. 11725: Springer, pp. 495–506, August 2019.
DOI: 10.1007/978-3-030-29400-7_35
“Massively Parallel Automated Software Tuning,”
48th International Conference on Parallel Processing (ICPP 2019), Kyoto, Japan, ACM Press, August 2019.
DOI: 10.1145/3337821.3337908 (911.88 KB)
(911.88 KB)
