Publications

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Journal Article
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)
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, 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., 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., 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)
Anzt, H., W. Sawyer, S. Tomov, P. Luszczek, and J. Dongarra, “Acceleration of GPU-based Krylov solvers via Data Transfer Reduction,” International Journal of High Performance Computing Applications, 2015.
Anzt, H., M. Baboulin, J. Dongarra, Y. Fournier, F. Hulsemann, A. Khabou, and Y. Wang, “Accelerating the Conjugate Gradient Algorithm with GPU in CFD Simulations,” VECPAR, 2016.
Conference Proceedings
Anzt, H., S. Tomov, J. Dongarra, and V. Heuveline, “Weighted Block-Asynchronous Iteration on GPU-Accelerated Systems,” Tenth International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Platforms (Best Paper), Rhodes Island, Greece, August 2012.  (764.02 KB)
Anzt, H., J. Dongarra, G. Flegar, E. S. Quintana-Orti, and A. E. Thomas, “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.  (512.57 KB)
Funk, Y., M. Götz, and H. Anzt, “Prediction of Optimal Solvers for Sparse Linear Systems Using Deep Learning,” 2022 SIAM Conference on Parallel Processing for Scientific Computing (PP), Philadelphia, PA, Society for Industrial and Applied Mathematics, pp. 14 - 24.
Tsai, Y. M., T. Cojean, and H. Anzt, “Porting Sparse Linear Algebra to Intel GPUs,” Euro-Par 2021: Parallel Processing Workshops, vol. 13098, Lisbon, Portugal, Springer International Publishing, pp. 57 - 68, June 2022.
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., 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.
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)
Conference Paper
Anzt, H., J. Dongarra, G. Flegar, and E. S. Quintana-Orti, “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.
Anzt, H., J. Dongarra, G. Flegar, and T. Gruetzmacher, “Variable-Size Batched Condition Number Calculation on GPUs,” SBAC-PAD, Lyon, France, September 2018.  (509.3 KB)
Anzt, H., J. Dongarra, and E. S. Quintana-Orti, “Tuning Stationary Iterative Solvers for Fault Resilience,” 6th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA15), Austin, TX, ACM, November 2015.  (1.28 MB)
Anzt, H., Y. Chen Chen, T. Cojean, J. Dongarra, G. Flegar, P. Nayak, E. S. Quintana-Orti, Y. M. Tsai, and W. Wang, “Towards Continuous Benchmarking,” Platform for Advanced Scientific Computing Conference (PASC 2019), Zurich, Switzerland, ACM Press, June 2019.  (1.51 MB)
Sukkari, D., M. Gates, M. Al Farhan, H. Anzt, and J. Dongarra, “Task-Based Polar Decomposition Using SLATE on Massively Parallel Systems with Hardware Accelerators,” SC-W '23: Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis, Denver, CO, ACM, November 2023.
Tsai, Y. M., T. Cojean, and H. Anzt, “Sparse Linear Algebra on AMD and NVIDIA GPUs—The Race is On,” ISC High Performance: Springer, June 2020.  (5.63 MB)
Anzt, H., D. Lukarski, S. Tomov, and J. Dongarra, “Self-Adaptive Multiprecision Preconditioners on Multicore and Manycore Architectures,” VECPAR 2014, Eugene, OR, June 2014.  (430.56 KB)
Luszczek, P., Y. Tsai, N. Lindquist, H. Anzt, and J. Dongarra, “Scalable Data Generation for Evaluating Mixed-Precision Solvers,” 2020 IEEE High Performance Extreme Computing Conference (HPEC), Waltham, MA, USA, IEEE, September 2020.  (1.3 MB)
Anzt, H., E. Chow, D. Szyld, and J. Dongarra, “Random-Order Alternating Schwarz for Sparse Triangular Solves,” 2015 SIAM Conference on Applied Linear Algebra (SIAM LA), Atlanta, GA, SIAM, October 2015.  (1.53 MB)
Aggarwal, I., P. Nayak, A. Kashi, and H. Anzt, “Preconditioners for Batched Iterative Linear Solvers on GPUs,” Smoky Mountains Computational Sciences and Engineering Conference, vol. 169075: Springer Nature Switzerland, pp. 38 - 53, January 2023.
Anzt, H., T. Ribizel, G. Flegar, E. Chow, and J. Dongarra, “ParILUT – A Parallel Threshold ILU for GPUs,” IEEE International Parallel and Distributed Processing Symposium (IPDPS), Rio de Janeiro, Brazil, IEEE, May 2019.  (505.95 KB)
Ribizel, T., and H. Anzt, “Parallel Symbolic Cholesky Factorization,” SC-W 2023: Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis, Denver, CO, ACM, November 2023.
Sid-Lakhdar, W., S. Cayrols, D. Bielich, A. Abdelfattah, P. Luszczek, M. Gates, S. Tomov, H. Johansen, D. Williams-Young, T. Davis, et al., “PAQR: Pivoting Avoiding QR factorization,” 2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS), St. Petersburg, FL, USA, IEEE, 2023.
Tomov, S., P. Luszczek, I. Yamazaki, J. Dongarra, H. Anzt, and W. Sawyer, “Optimizing Krylov Subspace Solvers on Graphics Processing Units,” Fourth International Workshop on Accelerators and Hybrid Exascale Systems (AsHES), IPDPS 2014, Phoenix, AZ, IEEE, May 2014.  (536.32 KB)
Goebel, F., H. Anzt, T. Cojean, G. Flegar, and E. S. Quintana-Orti, “Multiprecision Block-Jacobi for Iterative Triangular Solves,” European Conference on Parallel Processing (Euro-Par 2020): Springer, August 2020.
Tsai, Y-H. Mike, N. Beams, and H. Anzt, “Mixed Precision Algebraic Multigrid on GPUs,” Parallel Processing and Applied Mathematics (PPAM 2022), vol. 13826, Cham, Springer International Publishing, April 2023.
Anzt, H., and J. Dongarra, “A Jaccard Weights Kernel Leveraging Independent Thread Scheduling on GPUs,” SBAC-PAD, Lyon, France, IEEE, 2018.  (237.68 KB)
Anzt, H., E. Chow, and J. Dongarra, “Iterative Sparse Triangular Solves for Preconditioning,” EuroPar 2015, Vienna, Austria, Springer Berlin, August 2015.  (322.36 KB)
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)
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)
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., 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., 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)
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,” The Sixth International Workshop on Accelerators and Hybrid Exascale Systems (AsHES), Chicago, IL, IEEE, May 2016.  (285.28 KB)
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)
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)
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.

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