Publications

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Tech Report
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)
Presentation
Anzt, H., G. Collins, J. Dongarra, G. Flegar, and E. S. Quintana-Ortí, 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)
Journal Article
Anzt, H., J. Dongarra, G. Flegar, and E. S. Quintana-Ortí, Variable-Size Batched Gauss–Jordan Elimination for Block-Jacobi Preconditioning on Graphics Processors,” Parallel Computing, January 2018. DOI: 10.1016/j.parco.2017.12.006  (1.9 MB)
Anzt, H., G. Flegar, T. Gruetzmacher, and E. S. Quintana-Ortí, Toward a Modular Precision Ecosystem for High-Performance Computing,” The International Journal of High Performance Computing Applications, September 2019. DOI: 10.1177/1094342019846547  (1.93 MB)
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
Anzt, H., J. Dongarra, G. Flegar, N. J. Higham, and E. S. Quintana-Ortí, Adaptive Precision in Block‐Jacobi Preconditioning for Iterative Sparse Linear System Solvers,” Concurrency Computation: Practice and Experience, March 2018. DOI: 10.1002/cpe.4460
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, 2019. DOI: 10.1002/cpe.4460  (341.54 KB)
Conference Proceedings
Anzt, H., J. Dongarra, G. Flegar, E. S. Quintana-Ortí, 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.
Anzt, H., J. Dongarra, G. Flegar, and E. S. Quintana-Ortí, 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. DOI: 10.1145/3026937.3026940  (552.62 KB)
Conference Paper
Anzt, H., J. Dongarra, G. Flegar, and E. S. Quintana-Ortí, 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. DOI: 10.1109/ICPP.2017.18
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., Y-C. Chen, T. Cojean, J. Dongarra, G. Flegar, P. Nayak, E. S. Quintana-Orti, Y. M. Tsai, W. Wang, and , Towards Continuous Benchmarking,” the Platform for Advanced Scientific Computing ConferenceProceedings of the Platform for Advanced Scientific Computing Conference on - PASC '19, Zurich, SwitzerlandNew York, New York, USA, ACM Press, 2019. DOI: 10.1145/332498910.1145/3324989.3325719  (1.51 MB)
Anzt, H., G. Collins, J. Dongarra, G. Flegar, and E. S. Quintana-Ortí, Flexible Batched Sparse Matrix-Vector Product on GPUs,” Proceedings of the 8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA '17), Denver, Colorado, ACM Press, November 2017. DOI: 10.1145/3148226.3148230
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)2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Rio de Janeiro, Brazil, IEEE, 2019. DOI: 10.1109/IPDPSW.2019.00122  (622.32 KB)