Publications
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Optimizing Batch HGEMM on Small Sizes Using Tensor Cores
, San Jose, CA, GPU Technology Conference (GTC), March 2019.
(2.47 MB)
(2.47 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)
“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)
“Optimizing the SVD Bidiagonalization Process for a Batch of Small Matrices,”
International Conference on Computational Science (ICCS 2017), Zurich, Switzerland, Procedia Computer Science, June 2017.
DOI: 10.1016/j.procs.2017.05.237 (364.95 KB)
(364.95 KB)
“Out of Memory SVD Solver for Big Data,”
2017 IEEE High Performance Extreme Computing Conference (HPEC'17), Waltham, MA, IEEE, September 2017.
(1.33 MB)
(1.33 MB)
“PAQR: Pivoting Avoiding QR factorization,”
ICL Technical Report, no. ICL-UT-22-06, June 2022.
(364.85 KB)
(364.85 KB)
“Parallel Performance Measurement of Heterogeneous Parallel Systems with GPUs,”
International Conference on Parallel Processing (ICPP'11), Taipei, Taiwan, ACM, September 2011.
DOI: 10.1109/ICPP.2011.71 (1.41 MB)
(1.41 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)
“Performance Analysis of Parallel FFT on Large Multi-GPU Systems,”
2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Lyon, France, IEEE, August 2022.
DOI: 10.1109/IPDPSW55747.2022.00072
“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 (1.19 MB)
(1.19 MB)
“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)
“Performance Portability of a GPU Enabled Factorization with the DAGuE Framework,”
IEEE Cluster: workshop on Parallel Programming on Accelerator Clusters (PPAC), June 2011.
(290.98 KB)
(290.98 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)
“POMPEI: Programming with OpenMP4 for Exascale Investigations,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-17-09: University of Tennessee, December 2017.
(1.1 MB)
(1.1 MB)
“Power Aware Computing on GPUs,”
SAAHPC '12 (Best Paper Award), Argonne, IL, July 2012.
(658.06 KB)
(658.06 KB)
“Power-aware Computing: Measurement, Control, and Performance Analysis for Intel Xeon Phi,”
2017 IEEE High Performance Extreme Computing Conference (HPEC'17), Best Paper Finalist, Waltham, MA, IEEE, September 2017.
DOI: 10.1109/HPEC.2017.8091085 (908.84 KB)
(908.84 KB)
Power-aware Computing on GPGPUs
, Gatlinburg, TN, Fall Creek Falls Conference, Poster, September 2011.
(2.89 MB)
(2.89 MB)
Power-Aware HPC on Intel Xeon Phi KNL Processors
, Frankfurt, Germany, ISC High Performance (ISC17), Intel Booth Presentation, June 2017.
(5.87 MB)
(5.87 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)
“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 (4.4 MB)
(4.4 MB)
“Providing GPU Capability to LU and QR within the ScaLAPACK Framework,”
University of Tennessee Computer Science Technical Report (also LAWN 272), no. UT-CS-12-699, September 2012.
(7.48 MB)
(7.48 MB)
“A Python Library for Matrix Algebra on GPU and Multicore Architectures,”
2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS), Denver, CO, IEEE, December 2022.
DOI: 10.1109/MASS56207.2022.00121 (414.36 KB)
(414.36 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)
“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 (1.43 MB)
(1.43 MB)
“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)
“Sampling Algorithms to Update Truncated SVD,”
IEEE International Conference on Big Data, Boston, MA, IEEE, December 2017.
(700.79 KB)
(700.79 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
“A Scalable High Performant Cholesky Factorization for Multicore with GPU Accelerators,”
Proc. of VECPAR'10 (to appear), Berkeley, CA, June 2010.
(870.46 KB)
(870.46 KB)
Scheduling Cholesky Factorization on Multicore Architectures with GPU Accelerators
, Knoxville, TN, 2010 Symposium on Application Accelerators in High-Performance Computing (SAAHPC'10), Poster, July 2010.
(3.86 MB)
(3.86 MB)
“Self-Adaptive Multiprecision Preconditioners on Multicore and Manycore Architectures,”
VECPAR 2014, Eugene, OR, June 2014.
(430.56 KB)
(430.56 KB)
“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
