Performance, Design, and Autotuning of Batched GEMM for GPUs,” High Performance Computing: 31st International Conference, ISC High Performance 2016, Frankfurt, Germany, June 19-23, 2016, Proceedings, no. 9697: Springer International Publishing, pp. 21–38, 2016. DOI: 10.1007/978-3-319-41321-1_2“
Analyzing Performance of BiCGStab with Hierarchical Matrix on GPU Clusters,” IEEE International Parallel and Distributed Processing Symposium (IPDPS), Vancouver, BC, Canada, IEEE, May 2018.“
Batched Matrix Computations on Hardware Accelerators Based on GPUs,” 2015 SIAM Conference on Applied Linear Algebra (SIAM LA), Atlanta, GA, SIAM, October 2015.“
Design, Optimization, and Benchmarking of Dense Linear Algebra Algorithms on AMD GPUs,” 2020 IEEE High Performance Extreme Computing Virtual Conference: IEEE, September 2020.“
On the Development of Variable Size Batched Computation for Heterogeneous Parallel Architectures,” The 17th IEEE International Workshop on Parallel and Distributed Scientific and Engineering Computing (PDSEC 2016), IPDPS 2016, Chicago, IL, IEEE, May 2016.“
Fast Batched Matrix Multiplication for Small Sizes using Half Precision Arithmetic on GPUs,” 33rd IEEE International Parallel and Distributed Processing Symposium (IPDPS), Rio de Janeiro, Brazil, IEEE, May 2019.“
High-performance Cholesky Factorization for GPU-only Execution,” Proceedings of the General Purpose GPUs (GPGPU-10), Austin, TX, ACM, February 2017. DOI: 10.1145/3038228.3038237“
High-performance Matrix-matrix Multiplications of Very Small Matrices,” 22nd International European Conference on Parallel and Distributed Computing (Euro-Par'16), Grenoble, France, Springer International Publishing, August 2016.“
High-Performance Tensor Contractions for GPUs,” International Conference on Computational Science (ICCS'16), San Diego, CA, June 2016.“
Investigating the Benefit of FP16-Enabled Mixed-Precision Solvers for Symmetric Positive Definite Matrices using GPUs,” International Conference on Computational Science (ICCS 2020), Amsterdam, Netherlands, Springer, Cham, June 2020. DOI: 10.1007/978-3-030-50417-5_18“
Massively Parallel Automated Software Tuning,” 48th International Conference on Parallel Processing (ICPP 2019), Kyoto, Japan, ACM Press, August 2019. DOI: 10.1145/3337821.3337908“
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“
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.“
Performance, Design, and Autotuning of Batched GEMM for GPUs,” The International Supercomputing Conference (ISC High Performance 2016), Frankfurt, Germany, June 2016.“
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.“
Progressive Optimization of Batched LU Factorization on GPUs,” IEEE High Performance Extreme Computing Conference (HPEC’19), Waltham, MA, IEEE, September 2019.“
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.“
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.“
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“
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“
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“
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“
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“
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“
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“
Linear Algebra Software for Large-Scale Accelerated Multicore Computing,” Acta Numerica, vol. 25, pp. 1-160, May 2016. DOI: 10.1017/S0962492916000015“
MAGMA Templates for Scalable Linear Algebra on Emerging Architectures,” The International Journal of High Performance Computing Applications, vol. 2, issue 4, October 2020. DOI: 10.1177/1094342020938421“
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“
Optimizing Memory-Bound Numerical Kernels on GPU Hardware Accelerators,” VECPAR 2012, Kobe, Japan, July 2012.“
Parallel Programming Models for Dense Linear Algebra on Heterogeneous Systems,” Supercomputing Frontiers and Innovations, vol. 2, no. 4, October 2015. DOI: 10.14529/jsfi1504“
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“
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.
Cholesky Factorization on Batches of Matrices with Fixed and Variable Sizes , San Jose, CA, GPU Technology Conference (GTC16), Poster, April 2016.
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.
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.
MAtrix, TEnsor, and Deep-learning Optimized Routines (MATEDOR) , Washington, DC, NSF PI Meeting, Poster, April 2018. DOI: 10.6084/m9.figshare.6174143.v3
Optimizing Batch HGEMM on Small Sizes Using Tensor Cores , San Jose, CA, GPU Technology Conference (GTC), March 2019.
Tensor Contractions using Optimized Batch GEMM Routines , San Jose, CA, GPU Technology Conference (GTC), Poster, March 2018.
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.
Accelerating Tensor Contractions in High-Order FEM with MAGMA Batched , Atlanta, GA, SIAM Conference on Computer Science and Engineering (SIAM CSE17), Presentation, March 2017.
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.“
C++ API for Batch BLAS,” SLATE Working Notes, no. 4, ICL-UT-17-12: University of Tennessee, December 2017.“
C++ API for BLAS and LAPACK,” SLATE Working Notes, no. 2, ICL-UT-17-03: Innovative Computing Laboratory, University of Tennessee, June 2017.“
CEED ECP Milestone Report: Performance Tuning of CEED Software and 1st and 2nd Wave Apps : Zenodo, October 2019. DOI: 10.5281/zenodo.3477618
CEED ECP Milestone Report: Public release of CEED 2.0 : Zenodo, April 2019. DOI: 10.5281/zenodo.2641316
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.“
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.“