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Progressive Optimization of Batched LU Factorization on GPUs,” IEEE High Performance Extreme Computing Conference (HPEC’19), Waltham, MA, IEEE, September 2019.“
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.“
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“
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.
A Survey of Numerical Methods Utilizing Mixed Precision Arithmetic,” SLATE Working Notes, no. 15, ICL-UT-20-08: University of Tennessee, July 2020.“
Performance, Design, and Autotuning of Batched GEMM for GPUs,” The International Supercomputing Conference (ISC High Performance 2016), Frankfurt, Germany, June 2016.“
Implementation of the C++ API for Batch BLAS,” SLATE Working Notes, no. 7, ICL-UT-18-04: Innovative Computing Laboratory, University of Tennessee, June 2018.“
Optimizing Batch HGEMM on Small Sizes Using Tensor Cores , San Jose, CA, GPU Technology Conference (GTC), March 2019.
High-Performance Tensor Contractions for GPUs,” University of Tennessee Computer Science Technical Report, no. UT-EECS-16-738: University of Tennessee, January 2016.“
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.“
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.“
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“
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.“
C++ API for Batch BLAS,” SLATE Working Notes, no. 4, ICL-UT-17-12: University of Tennessee, December 2017.“
Cholesky Factorization on Batches of Matrices with Fixed and Variable Sizes , San Jose, CA, GPU Technology Conference (GTC16), Poster, April 2016.
Optimizing Memory-Bound Numerical Kernels on GPU Hardware Accelerators,” VECPAR 2012, Kobe, Japan, July 2012.“
SLATE Port to AMD and Intel Platforms,” SLATE Working Notes, no. 16, ICL-UT-21-01, April 2021.“
Roadmap for the Development of a Linear Algebra Library for Exascale Computing: SLATE: Software for Linear Algebra Targeting Exascale,” SLATE Working Notes, no. 1, ICL-UT-17-02: Innovative Computing Laboratory, University of Tennessee, June 2017.“
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“
Linear Algebra Software for Large-Scale Accelerated Multicore Computing,” Acta Numerica, vol. 25, pp. 1-160, May 2016. DOI: 10.1017/S0962492916000015“
High-Performance Tensor Contractions for GPUs,” International Conference on Computational Science (ICCS'16), San Diego, CA, June 2016.“
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“
Accelerating Tensor Contractions in High-Order FEM with MAGMA Batched , Atlanta, GA, SIAM Conference on Computer Science and Engineering (SIAM CSE17), Presentation, March 2017.
A Set of Batched Basic Linear Algebra Subprograms,” ACM Transactions on Mathematical Software, October 2020.“
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“
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.“
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.“
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 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“
Tensor Contractions using Optimized Batch GEMM Routines , San Jose, CA, GPU Technology Conference (GTC), Poster, March 2018.
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“
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“
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“