Publications

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Abdelfattah, A., M. Baboulin, V. Dobrev, J. Dongarra, C. Earl, J. Falcou, A. Haidar, I. Karlin, T. Kolev, I. Masliah, et al., 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)
Abdelfattah, A., A. Haidar, S. Tomov, and J. Dongarra, Batched One-Sided Factorizations of Tiny Matrices Using GPUs: Challenges and Countermeasures,” Journal of Computational Science, vol. 26, pp. 226–236, May 2018.  (3.73 MB)
Abdelfattah, A., M. Baboulin, V. Dobrev, J. Dongarra, C. Earl, J. Falcou, A. Haidar, I. Karlin, T. Kolev, I. Masliah, et al., 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)
Abdelfattah, A., P. Ghysels, W. Boukaram, S. Tomov, X. Sherry Li, and J. Dongarra, Addressing Irregular Patterns of Matrix Computations on GPUs and Their Impact on Applications Powered by Sparse Direct Solvers,” 2022 International Conference for High Performance Computing, Networking, Storage and Analysis (SC22), Dallas, TX, IEEE Computer Society, pp. 354-367, November 2022.  (1.57 MB)
Abdelfattah, A., J. Dongarra, D. Keyes, and H. Ltaeif, Optimizing Memory-Bound Numerical Kernels on GPU Hardware Accelerators,” VECPAR 2012, Kobe, Japan, July 2012.  (737.28 KB)
Abdelfattah, A., A. Haidar, S. Tomov, and J. Dongarra, 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.  (1.98 MB)
Abdelfattah, A., A. Haidar, S. Tomov, and J. Dongarra, 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.  (2.53 MB)
Abdelfattah, A., H. Anzt, E. G. Boman, E. Carson, T. Cojean, J. Dongarra, A. Fox, M. Gates, N. J. Higham, X. S. Li, et al., A survey of numerical linear algebra methods utilizing mixed-precision arithmetic,” The International Journal of High Performance Computing Applications, vol. 35, no. 4, pp. 344–369, 2021.
Abdelfattah, A., A. Haidar, S. Tomov, and J. Dongarra, 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)
Abdelfattah, A., S. Tomov, P. Luszczek, H. Anzt, and J. Dongarra, GPU-based LU Factorization and Solve on Batches of Matrices with Band Structure,” SC-W 2023: Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis, Denver, CO, ACM, November 2023.
Abdelfattah, A., S. Tomov, and J. Dongarra, 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.  (1.3 MB)
Abdelfattah, A., K. Arturov, C. Cecka, J. Dongarra, C. Freitag, M. Gates, A. Haidar, J. Kurzak, P. Luszczek, S. Tomov, et al., C++ API for Batch BLAS,” SLATE Working Notes, no. 04, ICL-UT-17-12: University of Tennessee, December 2017.  (1.89 MB)
Abdelfattah, A., M. Baboulin, V. Dobrev, J. Dongarra, A. Haidar, I. Karlin, T. Kolev, I. Masliah, and S. Tomov, 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)
Abdelfattah, A., M. Gates, J. Kurzak, P. Luszczek, and J. Dongarra, 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)
Abdelfattah, A., M. Baboulin, V. Dobrev, J. Dongarra, C. Earl, J. Falcou, A. Haidar, I. Karlin, T. Kolev, I. Masliah, et al., High-Performance Tensor Contractions for GPUs,” International Conference on Computational Science (ICCS'16), San Diego, CA, June 2016.  (2.36 MB)
Abdelfattah, A., A. Haidar, S. Tomov, and J. Dongarra, Factorization and Inversion of a Million Matrices using GPUs: Challenges and Countermeasures,” Procedia Computer Science, vol. 108, pp. 606–615, June 2017.  (643.44 KB)
Abdelfattah, A., A. Haidar, S. Tomov, and J. Dongarra, Cholesky Factorization on Batches of Matrices with Fixed and Variable Sizes , San Jose, CA, GPU Technology Conference (GTC16), Poster, April 2016.  (480.51 KB)
Abdelfattah, A., T. Costa, J. Dongarra, M. Gates, A. Haidar, S. Hammarling, N. J. Higham, J. Kurzak, P. Luszczek, S. Tomov, et al., A Set of Batched Basic Linear Algebra Subprograms,” ACM Transactions on Mathematical Software, October 2020.
Abdelfattah, A., J. Dongarra, A. Haidar, S. Tomov, and I. Yamazaki, 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)
Abdelfattah, A., V. Barra, N. Beams, R. Bleile, J. Brown, J-S. Camier, R. Carson, N. Chalmers, V. Dobrev, Y. Dudouit, et al., GPU algorithms for Efficient Exascale Discretizations,” Parallel Computing, vol. 108, pp. 102841, 2021.
Abalenkovs, M., N. Bagherpour, J. Dongarra, M. Gates, A. Haidar, J. Kurzak, P. Luszczek, S. Relton, J. Sistek, D. Stevens, et al., PLASMA 17 Performance Report,” Innovative Computing Laboratory Technical Report, no. ICL-UT-17-11: University of Tennessee, June 2017.  (7.57 MB)
Abalenkovs, M., N. Bagherpour, J. Dongarra, M. Gates, A. Haidar, J. Kurzak, P. Luszczek, S. Relton, J. Sistek, D. Stevens, et al., PLASMA 17.1 Functionality Report,” Innovative Computing Laboratory Technical Report, no. ICL-UT-17-10: University of Tennessee, June 2017.  (1.8 MB)
Abalenkovs, M., A. Abdelfattah, J. Dongarra, M. Gates, A. Haidar, J. Kurzak, P. Luszczek, S. Tomov, I. Yamazaki, and A. YarKhan, Parallel Programming Models for Dense Linear Algebra on Heterogeneous Systems,” Supercomputing Frontiers and Innovations, vol. 2, no. 4, October 2015.  (3.68 MB)
, The Future of Supercomputing: An Interim Report,” National Research Council, Washington, D.C., The National Academies Press, January 2003.

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