Matrix Multiplication on Batches of Small Matrices in Half and Half-Complex Precisions,” Journal of Parallel and Distributed Computing, vol. 145, pp. 188-201, November 2020. DOI: 10.1016/j.jpdc.2020.07.001“
Mixed-Precision Cholesky QR Factorization and its Case Studies on Multicore CPU with Multiple GPUs,” SIAM Journal on Scientific Computing, vol. 37, no. 3, pp. C203-C330, May 2015. DOI: DOI:10.1137/14M0973773“
Mixed-Precision Iterative Refinement using Tensor Cores on GPUs to Accelerate Solution of Linear Systems,” Proceedings of the Royal Society A, vol. 476, issue 2243, November 2020. DOI: 10.1098/rspa.2020.0110“
Model-Driven One-Sided Factorizations on Multicore, Accelerated Systems,” Supercomputing Frontiers and Innovations, vol. 1, issue 1, 2014. DOI: http://dx.doi.org/10.14529/jsfi1401“
Non-GPU-resident Dense Symmetric Indefinite Factorization,” Concurrency and Computation: Practice and Experience, November 2016. DOI: 10.1002/cpe.4012“
A Novel Hybrid CPU-GPU Generalized Eigensolver for Electronic Structure Calculations Based on Fine Grained Memory Aware Tasks,” Supercomputing '12 (poster), Salt Lake City, Utah, November 2012.“
A Novel Hybrid CPU-GPU Generalized Eigensolver for Electronic Structure Calculations Based on Fine Grained Memory Aware Tasks,” International Journal of High Performance Computing Applications, vol. 28, issue 2, pp. 196-209, May 2014. DOI: 10.1177/1094342013502097“
Parallel Programming Models for Dense Linear Algebra on Heterogeneous Systems,” Supercomputing Frontiers and Innovations, vol. 2, no. 4, October 2015. DOI: 10.14529/jsfi1504“
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“
Performance Portability of a GPU Enabled Factorization with the DAGuE Framework,” IEEE Cluster: workshop on Parallel Programming on Accelerator Clusters (PPAC), June 2011.“
Power Aware Computing on GPUs,” SAAHPC '12 (Best Paper Award), Argonne, IL, July 2012.“
Predicting the electronic properties of 3D, million-atom semiconductor nanostructure architectures,” J. Phys.: Conf. Ser. 46, vol. :101088/1742-6596/46/1/040, pp. 292-298, January 2006.“
Preliminary Results of Autotuning GEMM Kernels for the NVIDIA Kepler Architecture,” LAWN 267, 00 2012.“
Project-Based Research and Training in High Performance Data Sciences, Data Analytics, and Machine Learning,” The Journal of Computational Science Education, vol. 11, issue 1, pp. 36-44, January 2020. DOI: 10.22369/issn.2153-4136/11/1/7“
Prospectus for the Next LAPACK and ScaLAPACK Libraries,” PARA 2006, Umea, Sweden, June 2006.“
Reducing the Amount of out-of-core Data Access for GPU-Accelerated Randomized SVD,” Concurrency and Computation: Practice and Experience, April 2020. DOI: 10.1002/cpe.5754“
Scalability Study of a Quantum Simulation Code,” PARA 2010, Reykjavik, Iceland, June 2010.“
A Scalable High Performant Cholesky Factorization for Multicore with GPU Accelerators,” Proc. of VECPAR'10 (to appear), Berkeley, CA, June 2010.“
A Set of Batched Basic Linear Algebra Subprograms,” ACM Transactions on Mathematical Software, October 2020.“
A Set of Batched Basic Linear Algebra Subprograms and LAPACK Routines,” ACM Transactions on Mathematical Software (TOMS), vol. 47, no. 3, pp. 1–23, 2021. DOI: 10.1145/3431921“
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“
Soft Error Resilient QR Factorization for Hybrid System,” UT-CS-11-675 (also LAPACK Working Note #252), no. ICL-CS-11-675, July 2011.“
Soft Error Resilient QR Factorization for Hybrid System with GPGPU,” Journal of Computational Science, Seattle, WA, Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems at SC11, November 2011.“
Soft Error Resilient QR Factorization for Hybrid System with GPGPU,” Journal of Computational Science, vol. 4, issue 6, pp. 457–464, November 2013. DOI: http://dx.doi.org/10.1016/j.jocs.2013.01.004“
Solving Dense Symmetric Indefinite Systems using GPUs,” Concurrency and Computation: Practice and Experience, vol. 29, issue 9, March 2017. DOI: 10.1002/cpe.4055“
Solving Linear Diophantine Systems on Parallel Architectures,” IEEE Transactions on Parallel and Distributed Systems, vol. 30, issue 5, pp. 1158-1169, May 2019. DOI: http://dx.doi.org/10.1109/TPDS.2018.2873354“
Stability and Performance of Various Singular Value QR Implementations on Multicore CPU with a GPU,” ACM Transactions on Mathematical Software (TOMS), vol. 43, issue 2, October 2016.“
State-of-the-Art Eigensolvers for Electronic Structure Calculations of Large Scale Nano-Systems,” Journal of Computational Physics, vol. 227, no. 15, pp. 7113-7124, January 2008.“
Structure-aware Linear Solver for Realtime Convex Optimization for Embedded Systems,” IEEE Embedded Systems Letters, vol. 9, issue 3, pp. 61–64, May 2017. DOI: 10.1109/LES.2017.2700401“
Towards Dense Linear Algebra for Hybrid GPU Accelerated Manycore Systems,” Parallel Computing, vol. 36, no. 5-6, pp. 232-240, 00 2010.“
Translational process: Mathematical software perspective,” Journal of Computational Science, vol. 52, pp. 101216, 2021. DOI: 10.1016/j.jocs.2020.101216“
Translational Process: Mathematical Software Perspective,” Journal of Computational Science, September 2020. DOI: 10.1016/j.jocs.2020.101216“
Tridiagonalization of a dense symmetric matrix on multiple GPUs and its application to symmetric eigenvalue problems,” Concurrency and Computation: Practice and Experience, October 2013.“
The use of bulk states to accelerate the band edge state calculation of a semiconductor quantum dot,” Journal of Computational Physics (submitted), January 2006.“
The Use of Bulk States to Accelerate the Band Edge State Calculation of a Semiconductor Quantum Dot,” Journal of Computational Physics, vol. 223, pp. 774-782, 00 2007.“
Using MAGMA with PGI Fortran,” PGI Insider, November 2010.“
Using Mixed Precision for Sparse Matrix Computations to Enhance the Performance while Achieving 64-bit Accuracy,” ACM Transactions on Mathematical Software, vol. 34, no. 4, pp. 17-22, 00 2008.“
Accelerating 2D FFT: Exploit GPU Tensor Cores through Mixed-Precision , Dallas, TX, The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC18), ACM Student Research Poster, November 2018.
Accelerating FFT towards Exascale Computing : NVIDIA GPU Technology Conference (GTC2021), 2021.
Accelerating Tensor Contractions for High-Order FEM on CPUs, GPUs, and KNLs , Gatlinburg, TN, moky Mountains Computational Sciences and Engineering Conference (SMC16), Poster, September 2016.
Acceleration of the BLAST Hydro Code on GPU,” Supercomputing '12 (poster), Salt Lake City, Utah, SC12, November 2012.“
Cholesky Factorization on Batches of Matrices with Fixed and Variable Sizes , San Jose, CA, GPU Technology Conference (GTC16), Poster, April 2016.
Clover: Computational Libraries Optimized via Exascale Research , Houston, TX, 2020 Exascale Computing Project Annual Meeting, February 2020.
Enhancing the Performance of Dense Linear Algebra Solvers on GPUs (in the MAGMA Project) , Austin, TX, The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC08), November 2008.
FFT-ECP Fast Fourier Transform , Houston, TX, 2019 ECP Annual Meeting (Research Poster), January 2019.
GPUDirect MPI Communications and Optimizations to Accelerate FFTs on Exascale Systems,” EuroMPI'19 Posters, Zurich, Switzerland, no. icl-ut-19-06: ICL, September 2019.“
Harnessing GPU's Tensor Cores Fast FP16 Arithmetic to Speedup Mixed-Precision Iterative Refinement Solvers and Achieve 74 Gflops/Watt on Nvidia V100 , San Jose, CA, GPU Technology Conference (GTC), Poster, March 2018.