Publications
Experiences in Autotuning Matrix Multiplication for Energy Minimization on GPUs,”
Concurrency and Computation: Practice and Experience, vol. 27, issue 17, pp. 5096 - 5113, Oct 12, 2015.
DOI: 10.1002/cpe.3516
(1.99 MB)
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Experiences in autotuning matrix multiplication for energy minimization on GPUs,”
Concurrency in Computation: Practice and Experience, vol. 27, issue 17, pp. 5096-5113, December 2015.
DOI: 10.1002/cpe.3516
(1.98 MB)
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Fine-grained Bit-Flip Protection for Relaxation Methods,”
Journal of Computational Science, November 2016.
DOI: 10.1016/j.jocs.2016.11.013
(1.47 MB)
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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)
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Ginkgo: A Modern Linear Operator Algebra Framework for High Performance Computing,”
ACM Transactions on Mathematical Software, vol. 48, issue 12, pp. 1 - 33, March 2022.
DOI: 10.1145/3480935
(4.2 MB)
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Ginkgo—A math library designed for platform portability,”
Parallel Computing, vol. 111, pp. 102902, February 2022.
DOI: 10.1016/j.parco.2022.102902
“GPU-Accelerated Asynchronous Error Correction for Mixed Precision Iterative Refinement,”
EuroPar 2012 (also LAWN 260), Rhodes Island, Greece, August 2012.
(662.98 KB)
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Implementation and Tuning of Batched Cholesky Factorization and Solve for NVIDIA GPUs,”
IEEE Transactions on Parallel and Distributed Systems, no. 1045-9219, November 2015.
“Improving the Energy Efficiency of Sparse Linear System Solvers on Multicore and Manycore Systems,”
Philosophical Transactions of the Royal Society A -- Mathematical, Physical and Engineering Sciences, vol. 372, issue 2018, July 2014.
DOI: 10.1098/rsta.2013.0279
(779.57 KB)
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Incomplete Sparse Approximate Inverses for Parallel Preconditioning,”
Parallel Computing, vol. 71, pp. 1–22, January 2018.
DOI: 10.1016/j.parco.2017.10.003
(1.24 MB)
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Linear Algebra Software for Large-Scale Accelerated Multicore Computing,”
Acta Numerica, vol. 25, pp. 1-160, May 2016.
DOI: 10.1017/S0962492916000015
“Load-Balancing Sparse Matrix Vector Product Kernels on GPUs,”
ACM Transactions on Parallel Computing, vol. 7, issue 1, March 2020.
DOI: 10.1145/3380930
(5.67 MB)
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Optimization and Performance Evaluation of the IDR Iterative Krylov Solver on GPUs,”
The International Journal of High Performance Computing Applications, vol. 32, no. 2, pp. 220–230, March 2018.
DOI: 10.1177/1094342016646844
(2.08 MB)
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PAPI Software-Defined Events for in-Depth Performance Analysis,”
The International Journal of High Performance Computing Applications, vol. 33, issue 6, pp. 1113-1127, November 2019.
(442.39 KB)
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Parallel Selection on GPUs,”
Parallel Computing, vol. 91, March 2020, 2019.
DOI: 10.1016/j.parco.2019.102588
(1.43 MB)
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ParILUT - A New Parallel Threshold ILU,”
SIAM Journal on Scientific Computing, vol. 40, issue 4: SIAM, pp. C503–C519, July 2018.
DOI: 10.1137/16M1079506
(19.26 MB)
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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)
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Preconditioned Krylov Solvers on GPUs,”
Parallel Computing, June 2017.
DOI: 10.1016/j.parco.2017.05.006
(1.19 MB)
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Providing performance portable numerics for Intel GPUs,”
Concurrency and Computation: Practice and Experience, vol. 17, October 2022.
DOI: 10.1002/cpe.7400
(3.16 MB)
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Resiliency in numerical algorithm design for extreme scale simulations,”
The International Journal of High Performance Computing Applications, vol. 36371337212766180823, issue 2, pp. 251 - 285, March 2022.
DOI: 10.1177/10943420211055188
“A survey of numerical linear algebra methods utilizing mixed-precision arithmetic,”
The International Journal of High Performance Computing Applications, vol. 35, no. 4, pp. 344–369, 2021.
DOI: 10.1177/10943420211003313
“Toward a Modular Precision Ecosystem for High-Performance Computing,”
The International Journal of High Performance Computing Applications, vol. 33, issue 6, pp. 1069-1078, November 2019.
DOI: 10.1177/1094342019846547
(1.93 MB)
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Towards a New Peer Review Concept for Scientific Computing ensuring Technical Quality, Software Sustainability, and Result Reproducibility,”
Proceedings in Applied Mathematics and Mechanics, vol. 19, issue 1, November 2019.
DOI: 10.1002/pamm.201900490
“Unveiling the Performance-energy Trade-off in Iterative Linear System Solvers for Multithreaded Processors,”
Concurrency and Computation: Practice and Experience, vol. 27, issue 4, pp. 885-904, September 2014.
DOI: 10.1002/cpe.3341
(1.83 MB)
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Updating Incomplete Factorization Preconditioners for Model Order Reduction,”
Numerical Algorithms, vol. 73, issue 3, no. 3, pp. 611–630, February 2016.
DOI: 10.1007/s11075-016-0110-2
(565.34 KB)
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Using Jacobi Iterations and Blocking for Solving Sparse Triangular Systems in Incomplete Factorization Preconditioning,”
Journal of Parallel and Distributed Computing, vol. 119, pp. 219–230, November 2018.
DOI: 10.1016/j.jpdc.2018.04.017
(273.53 KB)
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Variable-Size Batched Gauss-Jordan Elimination for Block-Jacobi Preconditioning on Graphics Processors,”
Parallel Computing, vol. 81, pp. 131-146, January 2019.
DOI: 10.1016/j.parco.2017.12.006
(1.9 MB)
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Weighted Block-Asynchronous Relaxation for GPU-Accelerated Systems,”
SIAM Journal on Computing (submitted), March 2012.
(811.01 KB)
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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)
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Clover: Computational Libraries Optimized via Exascale Research
, Houston, TX, 2020 Exascale Computing Project Annual Meeting, February 2020.
(872 KB)

Gingko: A Sparse Linear Algebrea Library for HPC
: 2021 ECP Annual Meeting, April 2021.
(893.04 KB)

Ginkgo: A Node-Level Sparse Linear Algebra Library for HPC (Poster)
, Houston, TX, 2020 Exascale Computing Project Annual Meeting, February 2020.
(699 KB)

Flexible Batched Sparse Matrix Vector Product on GPUs
, Denver, Colorado, ScalA'17: 8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, November 2017.
(16.8 MB)

MAGMA MIC: Optimizing Linear Algebra for Intel Xeon Phi
, Frankfurt, Germany, ISC High Performance (ISC15), Intel Booth Presentation, June 2015.
(2.03 MB)

Accelerating the LOBPCG method on GPUs using a blocked Sparse Matrix Vector Product,”
University of Tennessee Computer Science Technical Report, no. UT-EECS-14-731: University of Tennessee, October 2014.
(1.83 MB)
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On block-asynchronous execution on GPUs,”
LAPACK Working Note, no. 291, November 2016.
(1.05 MB)
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A Block-Asynchronous Relaxation Method for Graphics Processing Units,”
University of Tennessee Computer Science Technical Report, no. UT-CS-11-687 / LAWN 258, November 2011.
(1.08 MB)
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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)
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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)
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MAGMA-sparse Interface Design Whitepaper,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-17-05, September 2017.
(1.28 MB)
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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)
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Software-Defined Events (SDEs) in MAGMA-Sparse,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-18-12: University of Tennessee, December 2018.
(481.69 KB)
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Solver Interface & Performance on Cori,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-18-05: University of Tennessee, June 2018.
(188.05 KB)
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A Survey of Numerical Methods Utilizing Mixed Precision Arithmetic,”
SLATE Working Notes, no. 15, ICL-UT-20-08: University of Tennessee, July 2020.
(3.98 MB)
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