Publications
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)
“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)
“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)
“Parallel Selection on GPUs,”
Parallel Computing, vol. 91, March 2020, 2019.
DOI: 10.1016/j.parco.2019.102588 (1.43 MB)
“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)
“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)
“Preconditioned Krylov Solvers on GPUs,”
Parallel Computing, June 2017.
DOI: 10.1016/j.parco.2017.05.006 (1.19 MB)
“Sparse matrix-vector and matrix-multivector products for the truncated SVD on graphics processors,”
Concurrency and Computation: Practice and Experience, August 2023.
DOI: 10.1002/cpe.7871
“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)
“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)
“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)
“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)
“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)
“Weighted Block-Asynchronous Relaxation for GPU-Accelerated Systems,”
SIAM Journal on Computing (submitted), March 2012.
(811.01 KB)
“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)
“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)
Earth Virtualization Engines - A Technical Perspective
, September 2023.
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)
“On block-asynchronous execution on GPUs,”
LAPACK Working Note, no. 291, November 2016.
(1.05 MB)
“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)
“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)
“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)
“MAGMA-sparse Interface Design Whitepaper,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-17-05, September 2017.
(1.28 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)
“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)
“Solver Interface & Performance on Cori,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-18-05: University of Tennessee, June 2018.
(188.05 KB)
“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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