Publications
“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)
(3.98 MB)
“Small Tensor Operations on Advanced Architectures for High-Order Applications,”
University of Tennessee Computer Science Technical Report, no. UT-EECS-17-749: Innovative Computing Laboratory, University of Tennessee, April 2017.
(1.09 MB)
(1.09 MB)
“SLATE Port to AMD and Intel Platforms,”
SLATE Working Notes, no. 16, ICL-UT-21-01, April 2021.
(890.75 KB)
(890.75 KB)
“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)
“Performance, Design, and Autotuning of Batched GEMM for GPUs,”
University of Tennessee Computer Science Technical Report, no. UT-EECS-16-739: University of Tennessee, February 2016.
(1.27 MB)
(1.27 MB)
“MAGMA Batched: A Batched BLAS Approach for Small Matrix Factorizations and Applications on GPUs,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-16-02: University of Tennessee, August 2016.
(929.79 KB)
(929.79 KB)
“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)
“High-Performance Tensor Contractions for GPUs,”
University of Tennessee Computer Science Technical Report, no. UT-EECS-16-738: University of Tennessee, January 2016.
(2.36 MB)
(2.36 MB)
“An Empirical View of SLATE Algorithms on Scalable Hybrid System,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-19-08: University of Tennessee, Knoxville, September 2019.
(441.16 KB)
(441.16 KB)
“Design, Optimization, and Benchmarking of Dense Linear Algebra Algorithms on AMD GPUs,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-20-12: University of Tennessee, August 2020.
(476.36 KB)
(476.36 KB)
CEED ECP Milestone Report: Public release of CEED 2.0
: Zenodo, April 2019.
DOI: 10.5281/zenodo.2641316 (4.98 MB)
(4.98 MB)
CEED ECP Milestone Report: Performance Tuning of CEED Software and 1st and 2nd Wave Apps
: Zenodo, October 2019.
DOI: 10.5281/zenodo.3477618 (8.31 MB)
(8.31 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)
“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)
“Algorithms and Optimization Techniques for High-Performance Matrix-Matrix Multiplications of Very Small Matrices,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-18-09: Innovative Computing Laboratory, University of Tennessee, September 2018.
(3.74 MB)
(3.74 MB)
Accelerating Tensor Contractions in High-Order FEM with MAGMA Batched
, Atlanta, GA, SIAM Conference on Computer Science and Engineering (SIAM CSE17), Presentation, March 2017.
(9.29 MB)
(9.29 MB)
Using GPU FP16 Tensor Cores Arithmetic to Accelerate Mixed-Precision Iterative Refinement Solvers and Reduce Energy Consumption
, Frankfurt, Germany, ISC High Performance (ISC18), Best Poster Award, June 2018.
(3.01 MB)
(3.01 MB)
Tensor Contractions using Optimized Batch GEMM Routines
, San Jose, CA, GPU Technology Conference (GTC), Poster, March 2018.
(1.64 MB)
(1.64 MB)
Optimizing Batch HGEMM on Small Sizes Using Tensor Cores
, San Jose, CA, GPU Technology Conference (GTC), March 2019.
(2.47 MB)
(2.47 MB)
MAtrix, TEnsor, and Deep-learning Optimized Routines (MATEDOR)
, Washington, DC, NSF PI Meeting, Poster, April 2018.
DOI: 10.6084/m9.figshare.6174143.v3 (2.4 MB)
(2.4 MB)
MATEDOR: MAtrix, TEnsor, and Deep-learning Optimized Routines
, Dallas, TX, The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC18), Research Poster, November 2018.
(2.55 MB)
(2.55 MB)
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.
(2.96 MB)
(2.96 MB)
Cholesky Factorization on Batches of Matrices with Fixed and Variable Sizes
, San Jose, CA, GPU Technology Conference (GTC16), Poster, April 2016.
(480.51 KB)
(480.51 KB)
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.
(4.29 MB)
(4.29 MB)
hipMAGMA v2.0
: Zenodo, July 2020.
DOI: 10.5281/zenodo.3928667
hipMAGMA v1.0
: Zenodo, March 2020.
DOI: 10.5281/zenodo.3908549
“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)
“A Set of Batched Basic Linear Algebra Subprograms,”
ACM Transactions on Mathematical Software, October 2020.
“Performance optimization of Sparse Matrix-Vector Multiplication for multi-component PDE-based applications using GPUs,”
Concurrency and Computation: Practice and Experience, vol. 28, issue 12, pp. 3447 - 3465, May 2016.
DOI: 10.1002/cpe.v28.1210.1002/cpe.3874 (3.21 MB)
(3.21 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)
“Optimizing Memory-Bound Numerical Kernels on GPU Hardware Accelerators,”
VECPAR 2012, Kobe, Japan, July 2012.
(737.28 KB)
(737.28 KB)
“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 (1.3 MB)
(1.3 MB)
“MAGMA Templates for Scalable Linear Algebra on Emerging Architectures,”
The International Journal of High Performance Computing Applications, vol. 34, issue 6, pp. 645-658, November 2020.
DOI: 10.1177/1094342020938421
“Linear Algebra Software for Large-Scale Accelerated Multicore Computing,”
Acta Numerica, vol. 25, pp. 1-160, May 2016.
DOI: 10.1017/S0962492916000015
“libCEED: Fast algebra for high-order element-based discretizations,”
Journal of Open Source Software, vol. 6, no. 63, pp. 2945, 2021.
DOI: 10.21105/joss.02945
“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)
“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)
“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)
“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)
“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)
“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)
“The Design of Fast and Energy-Efficient Linear Solvers: On the Potential of Half-Precision Arithmetic and Iterative Refinement Techniques,”
International Conference on Computational Science (ICCS 2018), vol. 10860, Wuxi, China, Springer, pp. 586–600, June 2018.
DOI: 10.1007/978-3-319-93698-7_45 (487.88 KB)
(487.88 KB)
“Using GPU FP16 Tensor Cores Arithmetic to Accelerate Mixed-Precision Iterative Refinement Solvers and Reduce Energy Consumption,”
ISC High Performance (ISC'18), Best Poster, Frankfurt, Germany, June 2018.
(3.01 MB)
(3.01 MB)
“Towards Half-Precision Computation for Complex Matrices: A Case Study for Mixed Precision Solvers on GPUs,”
ScalA19: 10th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, Denver, CO, IEEE, November 2019.
(523.87 KB) (3.42 MB)
(523.87 KB) (3.42 MB)
“Progressive Optimization of Batched LU Factorization on GPUs,”
IEEE High Performance Extreme Computing Conference (HPEC’19), Waltham, MA, IEEE, September 2019.
(299.38 KB)
(299.38 KB)
“Performance Tuning and Optimization Techniques of Fixed and Variable Size Batched Cholesky Factorization on GPUs,”
International Conference on Computational Science (ICCS'16), San Diego, CA, June 2016.
(626.21 KB)
(626.21 KB)
“Performance, Design, and Autotuning of Batched GEMM for GPUs,”
The International Supercomputing Conference (ISC High Performance 2016), Frankfurt, Germany, June 2016.
(1.27 MB)
(1.27 MB)
“Optimizing GPU Kernels for Irregular Batch Workloads: A Case Study for Cholesky Factorization,”
IEEE High Performance Extreme Computing Conference (HPEC’18), Waltham, MA, IEEE, September 2018.
(729.87 KB)
(729.87 KB)
“Novel HPC Techniques to Batch Execution of Many Variable Size BLAS Computations on GPUs,”
International Conference on Supercomputing (ICS '17), Chicago, Illinois, ACM, June 2017.
DOI: 10.1145/3079079.3079103 (1.04 MB)
(1.04 MB)
“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)
