Publications

Export 105 results:
Filters: Author is Anzt, Hartwig  [Clear All Filters]
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
I
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)
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)
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., 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)
Anzt, H., E. Chow, and J. Dongarra, “Iterative Sparse Triangular Solves for Preconditioning,” EuroPar 2015, Vienna, Austria, Springer Berlin, August 2015.  (322.36 KB)
J
Anzt, H., and J. Dongarra, “A Jaccard Weights Kernel Leveraging Independent Thread Scheduling on GPUs,” SBAC-PAD, Lyon, France, IEEE, 2018.  (237.68 KB)
L
Jagode, H., H. Anzt, H. Ltaief, and P. Luszczek, Lecture Notes in Computer Science: High Performance Computing , vol. 12761: Springer International Publishing, 2021.
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)
M
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)
Anzt, H., E. Boman, J. Dongarra, G. Flegar, M. Gates, M. Heroux, M. Hoemmen, J. Kurzak, P. Luszczek, S. Rajamanickam, et al., “MAGMA-sparse Interface Design Whitepaper,” Innovative Computing Laboratory Technical Report, no. ICL-UT-17-05, September 2017.  (1.28 MB)
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.
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.
O
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)
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)
P
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)
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.
Ribizel, T., and H. Anzt, “Parallel Selection on GPUs,” Parallel Computing, vol. 91, March 2020, 2019.  (1.43 MB)
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.
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., 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)
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)
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., M. Gates, J. Dongarra, M. Kreutzer, G. Wellein, and M. Kohler, “Preconditioned Krylov Solvers on GPUs,” Parallel Computing, June 2017.  (1.19 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.
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-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)
R
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)
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.
Abdelfattah, A., H. Anzt, A. Bouteiller, A. Danalis, J. Dongarra, M. Gates, A. Haidar, J. Kurzak, P. Luszczek, S. Tomov, et al., “Roadmap for the Development of a Linear Algebra Library for Exascale Computing: SLATE: Software for Linear Algebra Targeting Exascale,” SLATE Working Notes, no. 01, ICL-UT-17-02: Innovative Computing Laboratory, University of Tennessee, June 2017.  (2.8 MB)
S
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., 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)
Jagode, H., A. Danalis, H. Anzt, I. Yamazaki, M. Hoemmen, E. Boman, S. Tomov, and J. Dongarra, “Software-Defined Events (SDEs) in MAGMA-Sparse,” Innovative Computing Laboratory Technical Report, no. ICL-UT-18-12: University of Tennessee, December 2018.  (481.69 KB)
Anzt, H., I. Yamazaki, M. Hoemmen, E. Boman, and J. Dongarra, “Solver Interface & Performance on Cori,” Innovative Computing Laboratory Technical Report, no. ICL-UT-18-05: University of Tennessee, June 2018.  (188.05 KB)
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)
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.
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)
T
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-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.
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)
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)
U
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)
V
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, 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)

Pages