Publications
“A Systematic Multi-step Methodology for Performance Analysis of Communication Traces of Distributed Applications based on Hierarchical Clustering,”
Proc. of the 5th International Workshop on Performance Modeling, Evaluation, and Organization of Parallel and Distributed Systems (PMEO-PDS 2006), no. ICL-UT-05-06, Rhodes Island, Greece, IEEE Computer Society, April 2006.
(1.02 MB)
(1.02 MB)
“Numerical Linear Algebra on Emerging Architectures: The PLASMA and MAGMA Projects,”
Journal of Physics: Conference Series, vol. 180, 00 2009.
(119.37 KB)
(119.37 KB)
“LU Factorization for Accelerator-Based Systems,”
IEEE/ACS AICCSA 2011, Sharm-El-Sheikh, Egypt, December 2011.
(234.86 KB)
(234.86 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)
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)
Numerical Linear Algebra on Emerging Architectures: The PLASMA and MAGMA Projects
, Portland, OR, The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC09), November 2009.
(3.53 MB)
(3.53 MB)
“A Hybridization Methodology for High-Performance Linear Algebra Software for GPUs,”
in GPU Computing Gems, Jade Edition, vol. 2: Elsevier, pp. 473-484, 00 2011.
“Resiliency in numerical algorithm design for extreme scale simulations,”
The International Journal of High Performance Computing Applications, vol. 36371337212766180823, issue 2, pp. 251 - 285, March 2022.
DOI: 10.1177/10943420211055188
“Faster, Cheaper, Better - A Hybridization Methodology to Develop Linear Algebra Software for GPUs,”
LAPACK Working Note, no. 230, 00 2010.
(334.48 KB)
(334.48 KB)
“A Collection of White Papers from the BDEC2 Workshop in Bloomington, IN,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-18-15: University of Tennessee, Knoxville, November 2018.
(9.26 MB)
(9.26 MB)
“SLATE Performance Improvements: QR and Eigenvalues,”
SLATE Working Notes, no. 17, ICL-UT-21-02, April 2021.
(2 MB)
(2 MB)
“Sparse matrix-vector and matrix-multivector products for the truncated SVD on graphics processors,”
Concurrency and Computation: Practice and Experience, August 2023.
DOI: 10.1002/cpe.7871
“Compressed basis GMRES on high-performance graphics processing units,”
The International Journal of High Performance Computing Applications, May 2022.
DOI: 10.1177/10943420221115140 (13.52 MB)
(13.52 MB)
“Compression and load balancing for efficient sparse matrix‐vector product on multicore processors and graphics processing units,”
Concurrency and Computation: Practice and Experience, vol. 34, issue 14, June 2022.
DOI: 10.1002/cpe.6515 (749.82 KB)
(749.82 KB)
“Communication Avoiding LU with Tournament Pivoting in SLATE,”
SLATE Working Notes, no. 18, ICL-UT-22-01, January 2022.
(3.74 MB)
(3.74 MB)
“A Collection of White Papers from the BDEC2 Workshop in San Diego, CA,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-19-13: University of Tennessee, October 2019.
(8.25 MB)
(8.25 MB)
“Fast and Small Short Vector SIMD Matrix Multiplication Kernels for the CELL Processor,”
University of Tennessee Computer Science Technical Report, no. UT-CS-08-609, (also LAPACK Working Note 189), January 2008.
(500.99 KB)
(500.99 KB)
“Analysis and Optimization of Yee_Bench using Hardware Performance Counters,”
Proceedings of Parallel Computing 2005 (ParCo), Malaga, Spain, January 2005.
(72.27 KB)
(72.27 KB)
“A Collection of White Papers from the BDEC2 Workshop in Poznan, Poland,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-19-10: University of Tennessee, Knoxville, May 2019.
(5.82 MB)
(5.82 MB)
“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)
“Towards Continuous Benchmarking,”
Platform for Advanced Scientific Computing Conference (PASC 2019), Zurich, Switzerland, ACM Press, June 2019.
DOI: 10.1145/3324989.3325719 (1.51 MB)
(1.51 MB)
“Variable-Size Batched Gauss-Huard for Block-Jacobi Preconditioning,”
International Conference on Computational Science (ICCS 2017), vol. 108, Zurich, Switzerland, Procedia Computer Science, pp. 1783-1792, June 2017.
DOI: 10.1016/j.procs.2017.05.186 (512.57 KB)
(512.57 KB)
“AI Benchmarking for Science: Efforts from the MLCommons Science Working Group,”
Lecture Notes in Computer Science, vol. 13387: Springer International Publishing, pp. 47 - 64, January 2023.
DOI: 10.1007/978-3-031-23220-610.1007/978-3-031-23220-6_4
“Flexible Batched Sparse Matrix-Vector Product on GPUs,”
8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA '17), Denver, CO, ACM Press, November 2017.
DOI: http://dx.doi.org/10.1145/3148226.3148230 (583.4 KB)
(583.4 KB)
“Variable-Size Batched LU for Small Matrices and Its Integration into Block-Jacobi Preconditioning,”
46th International Conference on Parallel Processing (ICPP), Bristol, United Kingdom, IEEE, August 2017.
DOI: 10.1109/ICPP.2017.18
