Publications

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Anzt, H., E. Chow, and J. Dongarra, “Iterative Sparse Triangular Solves for Preconditioning,” EuroPar 2015, Vienna, Austria, Springer Berlin, August 2015.  (322.36 KB)
Anzt, H., T. Huckle, J. Bräckle, and J. Dongarra, “Incomplete Sparse Approximate Inverses for Parallel Preconditioning,” Parallel Computing, vol. 71, pp. 1–22, January 2018.  (1.24 MB)
Yamazaki, I., H. Anzt, S. Tomov, M. Hoemmen, and J. Dongarra, “Improving the performance of CA-GMRES on multicores with multiple GPUs,” IPDPS 2014, Phoenix, AZ, IEEE, May 2014.  (333.82 KB)
Anzt, H., and E. S. Quintana-Orti, “Improving the Energy Efficiency of Sparse Linear System Solvers on Multicore and Manycore Systems,” Philosophical Transactions of the Royal Society A -- Mathematical, Physical and Engineering Sciences, vol. 372, issue 2018, July 2014.  (779.57 KB)
Anzt, H., S. Tomov, and J. Dongarra, “Implementing a Sparse Matrix Vector Product for the SELL-C/SELL-C-σ formats on NVIDIA GPUs,” University of Tennessee Computer Science Technical Report, no. UT-EECS-14-727: University of Tennessee, April 2014.  (578.11 KB)
Kurzak, J., H. Anzt, M. Gates, and J. Dongarra, “Implementation and Tuning of Batched Cholesky Factorization and Solve for NVIDIA GPUs,” IEEE Transactions on Parallel and Distributed Systems, no. 1045-9219, November 2015.
H
Lukarski, D., H. Anzt, S. Tomov, and J. Dongarra, “Hybrid Multi-Elimination ILU Preconditioners on GPUs,” International Heterogeneity in Computing Workshop (HCW), IPDPS 2014, Phoenix, AZ, IEEE, May 2014.  (1.67 MB)
Anzt, H., T. Gruetzmacher, E. S. Quintana-Orti, and F. Scheidegger, “High-Performance GPU Implementation of PageRank with Reduced Precision based on Mantissa Segmentation,” 8th Workshop on Irregular Applications: Architectures and Algorithms, 2018.
Newburn, C. J., G. Bansal, M. Wood, L. Crivelli, J. Planas, A. Duran, P. Souza, L. Borges, P. Luszczek, S. Tomov, et al., “Heterogeneous Streaming,” The Sixth International Workshop on Accelerators and Hybrid Exascale Systems (AsHES), IPDPS 2016, Chicago, IL, IEEE, May 2016.  (2.73 MB)
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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.
Anzt, H., E. Ponce, G. D. Peterson, and J. Dongarra, “GPU-accelerated Co-design of Induced Dimension Reduction: Algorithmic Fusion and Kernel Overlap,” 2nd International Workshop on Hardware-Software Co-Design for High Performance Computing, Austin, TX, ACM, November 2015.  (1.46 MB)
Anzt, H., P. Luszczek, J. Dongarra, and V. Heuveline, “GPU-Accelerated Asynchronous Error Correction for Mixed Precision Iterative Refinement,” EuroPar 2012 (also LAWN 260), Rhodes Island, Greece, August 2012.  (662.98 KB)
Anzt, H., P. Luszczek, J. Dongarra, and V. Heuveline, “GPU-Accelerated Asynchronous Error Correction for Mixed Precision Iterative Refinement,” University of Tennessee Computer Science Technical Report UT-CS-11-690 (also Lawn 260), December 2011.  (662.98 KB)
Cojean, T., Y-H. Mike Tsai, and H. Anzt, “Ginkgo—A math library designed for platform portability,” Parallel Computing, vol. 111, pp. 102902, February 2022.
Anzt, H., T. Cojean, Y-C. Chen, F. Goebel, T. Gruetzmacher, P. Nayak, T. Ribizel, Y-H. Tsai, and J. Dongarra, Ginkgo: A Node-Level Sparse Linear Algebra Library for HPC (Poster) , Houston, TX, 2020 Exascale Computing Project Annual Meeting, February 2020.  (699 KB)
Anzt, H., T. Cojean, G. Flegar, F. Göbel, T. Grützmacher, P. Nayak, T. Ribizel, Y. Mike Tsai, and E. S. Quintana-Ortí, “Ginkgo: A Modern Linear Operator Algebra Framework for High Performance Computing,” ACM Transactions on Mathematical Software, vol. 48, issue 12, pp. 1 - 33, March 2022.  (4.2 MB)
Anzt, H., T. Cojean, Y-C. Chen, F. Goebel, T. Gruetzmacher, P. Nayak, T. Ribizel, and Y-H. Tsai, “Ginkgo: A High Performance Numerical Linear Algebra Library,” Journal of Open Source Software, vol. 5, issue 52, August 2020.  (721.84 KB)
Anzt, H., N. Beams, T. Cojean, F. Göbel, T. Grützmacher, A. Kashi, P. Nayak, T. Ribizel, and Y. M. Tsai, Gingko: A Sparse Linear Algebrea Library for HPC : 2021 ECP Annual Meeting, April 2021.  (893.04 KB)
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Anzt, H., G. Collins, J. Dongarra, G. Flegar, and E. S. Quintana-Orti, “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.  (583.4 KB)
Anzt, H., G. Collins, J. Dongarra, G. Flegar, and E. S. Quintana-Orti, Flexible Batched Sparse Matrix Vector Product on GPUs , Denver, Colorado, ScalA'17: 8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, November 2017.  (16.8 MB)
Anzt, H., J. Dongarra, and E. S. Quintana-Orti, “Fine-grained Bit-Flip Protection for Relaxation Methods,” Journal of Computational Science, November 2016.  (1.47 MB)
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Anzt, H., B. Haugen, J. Kurzak, P. Luszczek, and J. Dongarra, “Experiences in Autotuning Matrix Multiplication for Energy Minimization on GPUs,” Concurrency and Computation: Practice and Experience, vol. 27, issue 17, pp. 5096 - 5113, Oct 12, 2015.  (1.99 MB)
Anzt, H., B. Haugen, J. Kurzak, P. Luszczek, and J. Dongarra, “Experiences in autotuning matrix multiplication for energy minimization on GPUs,” Concurrency in Computation: Practice and Experience, vol. 27, issue 17, pp. 5096-5113, December 2015.  (1.98 MB)
Anzt, H., Y. M. Tsai, A. Abdelfattah, T. Cojean, and J. Dongarra, “Evaluating the Performance of NVIDIA’s A100 Ampere GPU for Sparse and Batched Computations,” 2020 IEEE/ACM Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS): IEEE, November 2020.  (1.9 MB)
Nayak, P., T. Cojean, and H. Anzt, “Evaluating Asynchronous Schwarz Solvers on GPUs,” International Journal of High Performance Computing Applications, August 2020.
Anzt, H., S. Tomov, and J. Dongarra, “Energy Efficiency and Performance Frontiers for Sparse Computations on GPU Supercomputers,” Sixth International Workshop on Programming Models and Applications for Multicores and Manycores (PMAM '15), San Francisco, CA, ACM, February 2015.  (2.29 MB)
Anzt, H., J. Dongarra, M. Kreutzer, G. Wellein, and M. Kohler, “Efficiency of General Krylov Methods on GPUs – An Experimental Study,” 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 683-691, May 2016.
Anzt, H., J. Dongarra, M. Kreutzer, G. Wellein, and M. Kohler, “Efficiency of General Krylov Methods on GPUs – An Experimental Study,” The Sixth International Workshop on Accelerators and Hybrid Exascale Systems (AsHES), Chicago, IL, IEEE, May 2016.  (285.28 KB)
Hoefler, T., B. Stevens, A. F. Prein, J. Baehr, T. Schulthess, T. F. Stocker, J. Taylor, D. Klocke, P. Manninen, P. M. Forster, et al., Earth Virtualization Engines - A Technical Perspective , September 2023.
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Anzt, H., E. Chow, D. Szyld, and J. Dongarra, “Domain Overlap for Iterative Sparse Triangular Solves on GPUs,” Software for Exascale Computing - SPPEXA, vol. 113: Springer International Publishing, pp. 527–545, September 2016.
C
Gruetzmacher, T., T. Cojean, G. Flegar, F. Göbel, and H. Anzt, “A Customized Precision Format Based on Mantissa Segmentation for Accelerating Sparse Linear Algebra,” Concurrency and Computation: Practice and Experience, vol. 40319, issue 262, January 2019.
Aliaga, J. I., H. Anzt, T. Grützmacher, E. S. Quintana-Orti, and A. E. Thomas, “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.  (749.82 KB)
Aliaga, J. I., H. Anzt, T. Grützmacher, E. S. Quintana-Ortí, and A. E. Thomas, “Compressed basis GMRES on high-performance graphics processing units,” The International Journal of High Performance Computing Applications, May 2022.  (13.52 MB)
Gates, M., S. Tomov, H. Anzt, P. Luszczek, and J. Dongarra, Clover: Computational Libraries Optimized via Exascale Research , Houston, TX, 2020 Exascale Computing Project Annual Meeting, February 2020.  (872 KB)
B
Anzt, H., J. Dongarra, M. Gates, J. Kurzak, P. Luszczek, S. Tomov, and I. Yamazaki, “Bringing High Performance Computing to Big Data Algorithms,” Handbook of Big Data Technologies: Springer, 2017.  (1.22 MB)
Anzt, H., S. Tomov, J. Dongarra, and V. Heuveline, “A Block-Asynchronous Relaxation Method for Graphics Processing Units,” Journal of Parallel and Distributed Computing, vol. 73, issue 12, pp. 1613–1626, December 2013.  (1.08 MB)
Anzt, H., S. Tomov, J. Dongarra, and V. Heuveline, “A Block-Asynchronous Relaxation Method for Graphics Processing Units,” University of Tennessee Computer Science Technical Report, no. UT-CS-11-687 / LAWN 258, November 2011.  (1.08 MB)
Anzt, H., S. Tomov, M. Gates, J. Dongarra, and V. Heuveline, “Block-asynchronous Multigrid Smoothers for GPU-accelerated Systems,” ICCS 2012, Omaha, NE, June 2012.  (608.95 KB)
Anzt, H., S. Tomov, M. Gates, J. Dongarra, and V. Heuveline, Block-asynchronous Multigrid Smoothers for GPU-accelerated Systems , no. UT-CS-11-689, December 2011.  (608.95 KB)
Anzt, H., E. Chow, and J. Dongarra, “On block-asynchronous execution on GPUs,” LAPACK Working Note, no. 291, November 2016.  (1.05 MB)
Kashi, A., P. Nayak, D. Kulkarni, A. Scheinberg, P. Lin, and H. Anzt, “Batched sparse iterative solvers on GPU for the collision operator for fusion plasma simulations,” 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS), Lyon, France, IEEE, July 2022.  (1.26 MB)
Anzt, H., E. Chow, T. Huckle, and J. Dongarra, “Batched Generation of Incomplete Sparse Approximate Inverses on GPUs,” Proceedings of the 7th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, pp. 49–56, November 2016.
Anzt, H., J. Dongarra, G. Flegar, and E. S. Quintana-Orti, “Batched Gauss-Jordan Elimination for Block-Jacobi Preconditioner Generation on GPUs,” Proceedings of the 8th International Workshop on Programming Models and Applications for Multicores and Manycores, New York, NY, USA, ACM, pp. 1–10, February 2017.  (552.62 KB)
A
Chow, E., H. Anzt, and J. Dongarra, “Asynchronous Iterative Algorithm for Computing Incomplete Factorizations on GPUs,” International Supercomputing Conference (ISC 2015), Frankfurt, Germany, July 2015.
Anzt, H., and G. Flegar, “Are we Doing the Right Thing? – A Critical Analysis of the Academic HPC Community,” 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Rio de Janeiro, Brazil, IEEE, May 2019.  (622.32 KB)
Anzt, H., M. Casas, C. I. Malossi, E. S. Quintana-Ortí, F. Scheidegger, and S. Zhuang, “Approximate Computing for Scientific Applications,” Approximate Computing Techniques, 322: Springer International Publishing, pp. 415 - 465, January 2022.
Ribizel, T., and H. Anzt, “Approximate and Exact Selection on GPUs,” 2019 IEEE International Parallel and Distributed Processing Symposium Workshops, Rio de Janeiro, Brazil, IEEE, May 2019.  (440.71 KB)
Thiyagalingam, J., G. von Laszewski, J. Yin, M. Emani, J. Papay, G. Barrett, P. Luszczek, A. Tsaris, C. Kirkpatrick, F. Wang, et al., “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.
Anzt, H., J. Dongarra, and E. S. Quintana-Orti, “Adaptive Precision Solvers for Sparse Linear Systems,” 3rd International Workshop on Energy Efficient Supercomputing (E2SC '15), Austin, TX, ACM, November 2015.
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.  (341.54 KB)

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