Publications
“Optimizing Symmetric Dense Matrix-Vector Multiplication on GPUs,”
ACM/IEEE Conference on Supercomputing (SC’11), Seattle, WA, November 2011.
(630.63 KB)
(630.63 KB)
“QR Factorization on a Multicore Node Enhanced with Multiple GPU Accelerators,”
Proceedings of IPDPS 2011, no. ICL-UT-10-04, Anchorage, AK, October 2010.
(468.17 KB)
(468.17 KB)
“Scalability Issues in FFT Computation,”
International Conference on Parallel Computing Technologies: Springer, pp. 279–287, 2021.
DOI: 10.1007/978-3-030-86359-3_21
“Some Issues in Dense Linear Algebra for Multicore and Special Purpose Architectures,”
PARA 2008, 9th International Workshop on State-of-the-Art in Scientific and Parallel Computing, Trondheim Norway, May 2008.
“Accelerating GPU Kernels for Dense Linear Algebra,”
Proc. of VECPAR'10, Berkeley, CA, June 2010.
(615.07 KB)
(615.07 KB)
“Accelerating Linear System Solutions Using Randomization Techniques,”
ACM Transactions on Mathematical Software (also LAWN 246), vol. 39, issue 2, February 2013.
DOI: 10.1145/2427023.2427025 (358.79 KB)
(358.79 KB)
“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)
(402.69 KB)
“Accelerating the Reduction to Upper Hessenberg, Tridiagonal, and Bidiagonal Forms through Hybrid GPU-Based Computing,”
Parallel Computing, vol. 36, no. 12, pp. 645-654, 00 2010.
(1.39 MB)
(1.39 MB)
“Accelerating the SVD Bi-Diagonalization of a Batch of Small Matrices using GPUs,”
Journal of Computational Science, vol. 26, pp. 237–245, May 2018.
DOI: 10.1016/j.jocs.2018.01.007 (2.18 MB)
(2.18 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)
“Algorithms and Optimization Techniques for High-Performance Matrix-Matrix Multiplications of Very Small Matrices,”
Parallel Computing, vol. 81, pp. 1–21, January 2019.
DOI: 10.1016/j.parco.2018.10.003 (3.27 MB)
(3.27 MB)
“Analysis and Design Techniques towards High-Performance and Energy-Efficient Dense Linear Solvers on GPUs,”
IEEE Transactions on Parallel and Distributed Systems, vol. 29, issue 12, pp. 2700–2712, December 2018.
DOI: 10.1109/TPDS.2018.2842785 (2.53 MB)
(2.53 MB)
“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)
(742.5 KB)
“Batched One-Sided Factorizations of Tiny Matrices Using GPUs: Challenges and Countermeasures,”
Journal of Computational Science, vol. 26, pp. 226–236, May 2018.
DOI: 10.1016/j.jocs.2018.01.005 (3.73 MB)
(3.73 MB)
“Block-asynchronous Multigrid Smoothers for GPU-accelerated Systems,”
ICCS 2012, Omaha, NE, June 2012.
(608.95 KB)
(608.95 KB)
“A Block-Asynchronous Relaxation Method for Graphics Processing Units,”
Journal of Parallel and Distributed Computing, vol. 73, issue 12, pp. 1613–1626, December 2013.
DOI: http://dx.doi.org/10.1016/j.jpdc.2013.05.008 (1.08 MB)
(1.08 MB)
“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)
“Divide and Conquer on Hybrid GPU-Accelerated Multicore Systems,”
SIAM Journal on Scientific Computing, vol. 34(2), pp. C70-C82, April 2012.
“Divide & Conquer on Hybrid GPU-Accelerated Multicore Systems,”
SIAM Journal on Scientific Computing (submitted), August 2010.
“Efficient exascale discretizations: High-order finite element methods,”
The International Journal of High Performance Computing Applications, pp. 10943420211020803, 2021.
DOI: 10.1177/10943420211020803
“Evaluation of Directive-Based Performance Portable Programming Models,”
International Journal of High Performance Computing and Networking, vol. 14, issue 2, pp. 165-182.
DOI: http://dx.doi.org/10.1504/IJHPCN.2017.10009064 (1.12 MB)
(1.12 MB)
“Exploiting Block Structures of KKT Matrices for Efficient Solution of Convex Optimization Problems,”
IEEE Access, 2021.
DOI: 10.1109/ACCESS.2021.3106054 (1.35 MB)
(1.35 MB)
“Factorization and Inversion of a Million Matrices using GPUs: Challenges and Countermeasures,”
Procedia Computer Science, vol. 108, pp. 606–615, June 2017.
DOI: 10.1016/j.procs.2017.05.250 (643.44 KB)
(643.44 KB)
“Fast Cholesky Factorization on GPUs for Batch and Native Modes in MAGMA,”
Journal of Computational Science, vol. 20, pp. 85–93, May 2017.
DOI: 10.1016/j.jocs.2016.12.009 (3.6 MB)
(3.6 MB)
“A Framework for Out of Memory SVD Algorithms,”
ISC High Performance 2017, pp. 158–178, June 2017.
DOI: 10.1007/978-3-319-58667-0_9 (393.22 KB)
(393.22 KB)
“From CUDA to OpenCL: Towards a Performance-portable Solution for Multi-platform GPU Programming,”
Parallel Computing, vol. 38, no. 8, pp. 391-407, August 2012.
(1.64 MB)
(1.64 MB)
“A Guide for Achieving High Performance with Very Small Matrices on GPUs: A Case Study of Batched LU and Cholesky Factorizations,”
IEEE Transactions on Parallel and Distributed Systems, vol. 29, issue 5, pp. 973–984, May 2018.
DOI: 10.1109/TPDS.2017.2783929 (832.92 KB)
(832.92 KB)
“Hybrid Multicore Cholesky Factorization with Multiple GPU Accelerators,”
IEEE Transaction on Parallel and Distributed Systems (submitted), March 2010.
(3.75 MB)
(3.75 MB)
“A Hybridization Methodology for High-Performance Linear Algebra Software for GPUs,”
in GPU Computing Gems, Jade Edition, vol. 2: Elsevier, pp. 473-484, 00 2011.
“The Impact of Multicore on Math Software,”
PARA 2006, Umea, Sweden, June 2006.
(223.53 KB)
(223.53 KB)
“An Improved MAGMA GEMM for Fermi GPUs,”
International Journal of High Performance Computing, vol. 24, no. 4, pp. 511-515, 00 2010.
“Investigating Power Capping toward Energy-Efficient Scientific Applications,”
Concurrency Computation: Practice and Experience, vol. 2018, issue e4485, pp. 1-14, April 2018.
DOI: 10.1002/cpe.4485 (1.2 MB)
(1.2 MB)
