Publications

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Hendrickson, B., P. Messina, B. Bland, J. Chen, P. Colella, E. Dart, J. Dongarra, T. Dunning, I. Foster, R. Gerber, et al., ASCR@40: Highlights and Impacts of ASCR’s Programs : US Department of Energy’s Office of Advanced Scientific Computing Research, June 2020. DOI: 10.2172/1631812
Hendrickson, B., P. Messina, B. Bland, J. Chen, P. Colella, E. Dart, J. Dongarra, T. Dunning, I. Foster, R. Gerber, et al., ASCR@40: Four Decades of Department of Energy Leadership in Advanced Scientific Computing Research : Advanced Scientific Computing Advisory Committee (ASCAC), US Department of Energy, August 2020.
Yamazaki, I., A. Abdelfattah, A. Ida, S. Ohshima, S. Tomov, R. Yokota, and J. Dongarra, “Analyzing Performance of BiCGStab with Hierarchical Matrix on GPU Clusters,” IEEE International Parallel and Distributed Processing Symposium (IPDPS), Vancouver, BC, Canada, IEEE, May 2018.  (1.37 MB)
Ayala, A., S. Tomov, P. Luszczek, S. Cayrols, G. Ragghianti, and J. Dongarra, “Analysis of the Communication and Computation Cost of FFT Libraries towards Exascale,” ICL Technical Report, no. ICL-UT-22-07: Innovative Computing Laboratory, July 2022.  (5.91 MB)
Haidar, A., H. Ltaeif, A. YarKhan, and J. Dongarra, “Analysis of Dynamically Scheduled Tile Algorithms for Dense Linear Algebra on Multicore Architectures,” University of Tennessee Computer Science Technical Report, UT-CS-11-666, (also Lawn 243), March 2011.  (1.65 MB)
Haidar, A., H. Ltaeif, A. YarKhan, and J. Dongarra, “Analysis of Dynamically Scheduled Tile Algorithms for Dense Linear Algebra on Multicore Architectures,” Submitted to Concurrency and Computations: Practice and Experience, November 2010.  (1.65 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. DOI: 10.1109/TPDS.2018.2842785  (2.53 MB)
Masliah, I., A. Abdelfattah, A. Haidar, S. Tomov, M. Baboulin, J. Falcou, and J. Dongarra, “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.  (3.74 MB)
Masliah, I., A. Abdelfattah, A. Haidar, S. Tomov, M. Baboulin, J. Falcou, and J. Dongarra, “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  (3.27 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)
Anzt, H., J. Dongarra, G. Flegar, N. J. Higham, and E. S. Quintana-Orti, “Adaptive Precision in Block-Jacobi Preconditioning for Iterative Sparse Linear System Solvers,” Concurrency and Computation: Practice and Experience, vol. 31, no. 6, pp. e4460, March 2019. DOI: 10.1002/cpe.4460  (341.54 KB)
Luo, X., W. Wu, G. Bosilca, T. Patinyasakdikul, L. Wang, and J. Dongarra, “ADAPT: An Event-Based Adaptive Collective Communication Framework,” The 27th International Symposium on High-Performance Parallel and Distributed Computing (HPDC '18), Tempe, Arizona, ACM Press, June 2018. DOI: 10.1145/3208040.3208054  (493.65 KB)
Dongarra, J., M. Faverge, H. Ltaeif, and P. Luszczek, “Achieving Numerical Accuracy and High Performance using Recursive Tile LU Factorization,” University of Tennessee Computer Science Technical Report (also as a LAWN), no. ICL-UT-11-08, September 2011.  (618.53 KB)
Dongarra, J., M. Faverge, H. Ltaeif, and P. Luszczek, “Achieving numerical accuracy and high performance using recursive tile LU factorization with partial pivoting,” Concurrency and Computation: Practice and Experience, vol. 26, issue 7, pp. 1408-1431, May 2014. DOI: 10.1002/cpe.3110  (1.96 MB)
Dongarra, J., S. Moore, P. Mucci, K. Seymour, and H. You, “Accurate Cache and TLB Characterization Using Hardware Counters,” International Conference on Computational Science (ICCS 2004), Krakow, Poland, Springer, June 2004. DOI: 10.1007/978-3-540-24688-6_57  (167.1 KB)
Gates, M., S. Tomov, and J. Dongarra, “Accelerating the SVD Two Stage Bidiagonal Reduction and Divide and Conquer Using GPUs,” Parallel Computing, vol. 74, pp. 3–18, May 2018. DOI: 10.1016/j.parco.2017.10.004  (1.34 MB)
Dong, T., A. Haidar, S. Tomov, and J. Dongarra, “Accelerating the SVD Bi-Diagonalization of a Batch of Small Matrices using GPUs,” Journal of Computational Science, vol. 26, pp. 237–245, May 2018. DOI: 10.1016/j.jocs.2018.01.007  (2.18 MB)
Tomov, S., R. Nath, and J. Dongarra, “Accelerating the Reduction to Upper Hessenberg, Tridiagonal, and Bidiagonal Forms through Hybrid GPU-Based Computing,” Parallel Computing, vol. 36, no. 12, pp. 645-654, 00 2010.  (1.39 MB)
Tomov, S., and J. Dongarra, “Accelerating the Reduction to Upper Hessenberg Form through Hybrid GPU-Based Computing,” University of Tennessee Computer Science Technical Report, UT-CS-09-642 (also LAPACK Working Note 219), May 2009.  (2.37 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)

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