Publications

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Journal Article
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., 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)
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)
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)
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.
Nayak, P., T. Cojean, and H. Anzt, “Evaluating Asynchronous Schwarz Solvers on GPUs,” International Journal of High Performance Computing Applications, August 2020.
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., 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., J. Dongarra, and E. S. Quintana-Orti, “Fine-grained Bit-Flip Protection for Relaxation Methods,” Journal of Computational Science, November 2016.  (1.47 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., 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)
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., 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)
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.
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., 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)
Abdelfattah, A., H. Anzt, J. Dongarra, M. Gates, A. Haidar, J. Kurzak, P. Luszczek, S. Tomov, , and A. YarKhan, “Linear Algebra Software for Large-Scale Accelerated Multicore Computing,” Acta Numerica, vol. 25, pp. 1-160, May 2016.
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.  (5.67 MB)
Anzt, H., M. Kreutzer, E. Ponce, G. D. Peterson, G. Wellein, and J. Dongarra, “Optimization and Performance Evaluation of the IDR Iterative Krylov Solver on GPUs,” The International Journal of High Performance Computing Applications, vol. 32, no. 2, pp. 220–230, March 2018.  (2.08 MB)
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.  (1.43 MB)
Anzt, H., E. Chow, and J. Dongarra, “ParILUT - A New Parallel Threshold ILU,” SIAM Journal on Scientific Computing, vol. 40, issue 4: SIAM, pp. C503–C519, July 2018.  (19.26 MB)
Anzt, H., S. Tomov, and J. Dongarra, “On the performance and energy efficiency of sparse linear algebra on GPUs,” International Journal of High Performance Computing Applications, October 2016.  (1.19 MB)
Anzt, H., M. Gates, J. Dongarra, M. Kreutzer, G. Wellein, and M. Kohler, “Preconditioned Krylov Solvers on GPUs,” Parallel Computing, June 2017.  (1.19 MB)
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.  (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.
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.
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.
Tsai, Y-H. Mike, N. Beams, and H. Anzt, “Three-precision algebraic multigrid on GPUs,” Future Generation Computer Systems, July 2023.
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.  (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.
Aliaga, J. I., H. Anzt, M. Castillo, J. C. Fernández, G. León, J. Pérez, and E. S. Quintana-Orti, “Unveiling the Performance-energy Trade-off in Iterative Linear System Solvers for Multithreaded Processors,” Concurrency and Computation: Practice and Experience, vol. 27, issue 4, pp. 885-904, September 2014.  (1.83 MB)
Anzt, H., E. Chow, J. Saak, and J. Dongarra, “Updating Incomplete Factorization Preconditioners for Model Order Reduction,” Numerical Algorithms, vol. 73, issue 3, no. 3, pp. 611–630, February 2016.  (565.34 KB)
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.
Chow, E., H. Anzt, J. Scott, and J. Dongarra, “Using Jacobi Iterations and Blocking for Solving Sparse Triangular Systems in Incomplete Factorization Preconditioning,” Journal of Parallel and Distributed Computing, vol. 119, pp. 219–230, November 2018.  (273.53 KB)
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.  (1.9 MB)
Anzt, H., J. Dongarra, and V. Heuveline, “Weighted Block-Asynchronous Relaxation for GPU-Accelerated Systems,” SIAM Journal on Computing (submitted), March 2012.  (811.01 KB)
Dongarra, J., S. Tomov, P. Luszczek, J. Kurzak, M. Gates, I. Yamazaki, H. Anzt, A. Haidar, and A. Abdelfattah, “With Extreme Computing, the Rules Have Changed,” Computing in Science & Engineering, vol. 19, issue 3, pp. 52-62, May 2017.  (485.34 KB)
Poster
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)
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)
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)
Preprint
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.
Presentation
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, M. Gates, A. Haidar, K. Kabir, P. Luszczek, S. Tomov, and I. Yamazaki, MAGMA MIC: Optimizing Linear Algebra for Intel Xeon Phi , Frankfurt, Germany, ISC High Performance (ISC15), Intel Booth Presentation, June 2015.  (2.03 MB)
Tech Report
Anzt, H., S. Tomov, and J. Dongarra, “Accelerating the LOBPCG method on GPUs using a blocked Sparse Matrix Vector Product,” University of Tennessee Computer Science Technical Report, no. UT-EECS-14-731: University of Tennessee, October 2014.  (1.83 MB)
Anzt, H., E. Chow, and J. Dongarra, “On block-asynchronous execution on GPUs,” LAPACK Working Note, no. 291, November 2016.  (1.05 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., 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)
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)

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