Publications

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2024
Lin, P. T., P. Nayak, A. Kashi, D. Kulkarni, A. Scheinberg, and H. Anzt, “Accelerating Fusion Plasma Collision Operator Solves with Portable Batched Iterative Solvers on GPUs,” ISC High Performance 2024 International Workshops , vol. 15058, Hamburg, Germany, Springer, Cham, pp. 127 - 140, December 2024. DOI: 10.1007/978-3-031-73716-9
Luszczek, P., A. Abdelfattah, H. Anzt, A. Suzuki, and S. Tomov, “Batched sparse and mixed-precision linear algebra interface for efficient use of GPU hardware accelerators in scientific applications,” Future Generation Computer Systems, vol. 160, pp. 359 - 374, November 2024. DOI: 10.1016/j.future.2024.06.004
Cojean, T., P. Nayak, T. Ribizel, N. Beams, Y-H. Mike Tsai, M. Koch, F. Göbel, T. Grützmacher, and H. Anzt, “Ginkgo - A math library designed to accelerate Exascale Computing Project science applications,” The International Journal of High Performance Computing Applications, August 2024. DOI: 10.1177/10943420241268323
Abdelfattah, A., W. Ahrens, H. Anzt, C. Armstrong, B. Brock, A. Buluc, F. Busato, T. Cojean, T. Davis, J. Demmel, et al., Interface for Sparse Linear Algebra Operations , November 2024. DOI: 10.48550/arXiv.2411.13259
Anzt, H., A. Huebl, and X. S. Li, “Then and Now: Improving Software Portability, Productivity, and 100× Performance,” Computing in Science & Engineering, pp. 1 - 10, April 2024. DOI: 10.1109/MCSE.2024.3387302
2023
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. DOI: 10.1007/978-3-031-23220-610.1007/978-3-031-23220-6_4
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.
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. DOI: 10.1145/3624062.3624247
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. DOI: 10.1007/978-3-031-30442-2_9
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. DOI: 10.1109/IPDPS54959.2023.00040
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. DOI: 10.1145/3624062.3624253
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. DOI: 10.1007/978-3-031-23606-810.1007/978-3-031-23606-8_3
Aliaga, J. I., H. Anzt, E. S. Quintana-Orti, and A. E. Thomas, “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
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. DOI: 10.1145/3624062.3624248
Tsai, Y-H. Mike, N. Beams, and H. Anzt, “Three-precision algebraic multigrid on GPUs,” Future Generation Computer Systems, July 2023. DOI: 10.1016/j.future.2023.07.024
Grützmacher, T., H. Anzt, and E. S. Quintana‐Ortí, “Using Ginkgo's memory accessor for improving the accuracy of memory‐bound low precision BLAS,” Software: Practice and Experience, vol. 532, issue 1, pp. 81 - 98, January Jan. DOI: 10.1002/spe.v53.110.1002/spe.3041
2022
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. DOI: 10.1007/978-3-030-94705-7_14
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. DOI: 10.1109/IPDPS53621.2022.00024  (1.26 MB)
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. DOI: 10.1177/10943420221115140  (13.52 MB)
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. DOI: 10.1002/cpe.6515  (749.82 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. DOI: 10.1145/3480935  (4.2 MB)
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. DOI: 10.1016/j.parco.2022.102902
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. DOI: 10.1007/978-3-031-06156-1_5
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. DOI: 10.1137/1.978161197714110.1137/1.9781611977141.2
Tsai, Y-H. M., T. Cojean, and H. Anzt, “Providing performance portable numerics for Intel GPUs,” Concurrency and Computation: Practice and Experience, vol. 17, October 2022. DOI: 10.1002/cpe.7400  (3.16 MB)
Agullo, E., M. Altenbernd, H. Anzt, L. Bautista-Gomez, T. Benacchio, L. Bonaventura, H-J. Bungartz, S. Chatterjee, F. M. Ciorba, N. DeBardeleben, et al., “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
2021
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)
Jagode, H., H. Anzt, H. Ltaief, and P. Luszczek, Lecture Notes in Computer Science: High Performance Computing , vol. 12761: Springer International Publishing, 2021. DOI: 10.1007/978-3-030-90539-2
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. DOI: 10.1177/10943420211003313
2020
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)
Nayak, P., T. Cojean, and H. Anzt, “Evaluating Asynchronous Schwarz Solvers on GPUs,” International Journal of High Performance Computing Applications, August 2020. DOI: 10.1177/1094342020946814
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., 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. DOI: 10.21105/joss.02260  (721.84 KB)
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, C. Yen-Chen, J. Dongarra, G. Flegar, P. Nayak, S. Tomov, Y. M. Tsai, and W. Wang, “Load-Balancing Sparse Matrix Vector Product Kernels on GPUs,” ACM Transactions on Parallel Computing, vol. 7, issue 1, March 2020. DOI: 10.1145/3380930  (5.67 MB)
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. DOI: 10.1007/978-3-030-57675-2_34
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. DOI: 10.1109/HPEC43674.2020.9286145  (1.3 MB)
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. DOI: 10.1007/978-3-030-50743-5_16  (5.63 MB)
Abdelfattah, A., H. Anzt, E. Boman, E. Carson, T. Cojean, J. Dongarra, M. Gates, T. Gruetzmacher, N. J. Higham, S. Li, et al., “A Survey of Numerical Methods Utilizing Mixed Precision Arithmetic,” SLATE Working Notes, no. 15, ICL-UT-20-08: University of Tennessee, July 2020.  (3.98 MB)
2019
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)
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. DOI: 10.1109/IPDPSW.2019.00088  (440.71 KB)
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. DOI: 10.1109/IPDPSW.2019.00122  (622.32 KB)
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. DOI: 10.1002/cpe.5418
Jagode, H., A. Danalis, H. Anzt, and J. Dongarra, “PAPI Software-Defined Events for in-Depth Performance Analysis,” The International Journal of High Performance Computing Applications, vol. 33, issue 6, pp. 1113-1127, November 2019.  (442.39 KB)
Ribizel, T., and H. Anzt, “Parallel Selection on GPUs,” Parallel Computing, vol. 91, March 2020, 2019. DOI: 10.1016/j.parco.2019.102588  (1.43 MB)
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. DOI: 10.1109/IPDPS.2019.00033  (505.95 KB)
Anzt, H., G. Flegar, T. Gruetzmacher, and E. S. Quintana-Orti, “Toward a Modular Precision Ecosystem for High-Performance Computing,” The International Journal of High Performance Computing Applications, vol. 33, issue 6, pp. 1069-1078, November 2019. DOI: 10.1177/1094342019846547  (1.93 MB)
Anzt, H., T. Cojean, and E. Kuhn, “Towards a New Peer Review Concept for Scientific Computing ensuring Technical Quality, Software Sustainability, and Result Reproducibility,” Proceedings in Applied Mathematics and Mechanics, vol. 19, issue 1, November 2019. DOI: 10.1002/pamm.201900490
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. DOI: 10.1145/3324989.3325719  (1.51 MB)
Anzt, H., J. Dongarra, G. Flegar, and E. S. Quintana-Orti, “Variable-Size Batched Gauss-Jordan Elimination for Block-Jacobi Preconditioning on Graphics Processors,” Parallel Computing, vol. 81, pp. 131-146, January 2019. DOI: 10.1016/j.parco.2017.12.006  (1.9 MB)

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