Publications

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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.
Aliaga, J. I., H. Anzt, M. Castillo, J. C. Fernández, G. León, J. Pérez, and E. S. Quintana-Ortí, 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)
Computational Science – ICCS 2009, Proceedings of the 9th International Conference,” Lecture Notes in Computer Science: Theoretical Computer Science and General Issues, vol. -, no. 5544-5545, Baton Rouge, LA, May 2009.
Alvaro, W., J. Kurzak, and J. Dongarra, Optimizing Matrix Multiplication for a Short-Vector SIMD Architecture - CELL Processor,” Parallel Computing, vol. 35, pp. 138-150, 00-2009.  (591.16 KB)
Alvaro, W., J. Kurzak, and J. Dongarra, Fast and Small Short Vector SIMD Matrix Multiplication Kernels for the CELL Processor,” University of Tennessee Computer Science Technical Report, no. UT-CS-08-609, (also LAPACK Working Note 189), January 2008.  (500.99 KB)
Anderson, E., Z. Bai, C. Bischof, S. Blackford, J. Demmel, J. Dongarra, J. Du Croz, A. Greenbaum, S. Hammarling, A. McKenney, et al., LAPACK Users' Guide, 3rd ed.,” Philadelphia: Society for Industrial and Applied Mathematics, January 1999.
Andersson, U., and P. Mucci, Analysis and Optimization of Yee_Bench using Hardware Performance Counters,” Proceedings of Parallel Computing 2005 (ParCo) (to appear), Malaga, Spain, January 2005.  (72.27 KB)
Angskun, T., G. Bosilca, and J. Dongarra, Self-Healing in Binomial Graph Networks,” 2nd International Workshop On Reliability in Decentralized Distributed Systems (RDDS 2007), Vilamoura, Algarve, Portugal, November 2007.  (322.39 KB)
Angskun, T., G. Fagg, G. Bosilca, J. Pjesivac–Grbovic, and J. Dongarra, Self-Healing Network for Scalable Fault-Tolerant Runtime Environments,” Future Generation Computer Systems, vol. 26, no. 3, pp. 479-485, March 2010.  (1.54 MB)
Angskun, T., G. Bosilca, and J. Dongarra, Binomial Graph: A Scalable and Fault- Tolerant Logical Network Topology,” Proceedings of The Fifth International Symposium on Parallel and Distributed Processing and Applications (ISPA07), Niagara Falls, Canada, Springer, August 2007.  (480.47 KB)
Angskun, T., G. Fagg, G. Bosilca, J. Pjesivac–Grbovic, and J. Dongarra, Scalable Fault Tolerant Protocol for Parallel Runtime Environments,” 2006 Euro PVM/MPI, no. ICL-UT-06-12, Bonn, Germany, 00-2006.  (149.07 KB)
Angskun, T., G. Bosilca, G. Fagg, J. Pjesivac–Grbovic, and J. Dongarra, Reliability Analysis of Self-Healing Network using Discrete-Event Simulation,” Proceedings of Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid '07): IEEE Computer Society, pp. 437-444, May 2007.
Angskun, T., G. Bosilca, B. Vander Zanden, and J. Dongarra, Optimal Routing in Binomial Graph Networks,” The International Conference on Parallel and Distributed Computing, applications and Technologies (PDCAT), Adelaide, Australia, IEEE Computer Society, December 2007.
Angskun, T., G. Fagg, G. Bosilca, J. Pjesivac–Grbovic, and J. Dongarra, Self-Healing Network for Scalable Fault Tolerant Runtime Environments,” DAPSYS 2006, 6th Austrian-Hungarian Workshop on Distributed and Parallel Systems, Innsbruck, Austria, January 2006.  (162.83 KB)
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., 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., 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.  (552.62 KB)
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.
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., J. Dongarra, and E. S. Quintana-Ortí, Adaptive Precision Solvers for Sparse Linear Systems,” 3rd International Workshop on Energy Efficient Supercomputing (E2SC '15), Austin, TX, ACM, November 2015.
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., M. Gates, J. Dongarra, M. Kreutzer, G. Wellein, and M. Kohler, Preconditioned Krylov Solvers on GPUs,” Parallel Computing, June 2017.
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.  (341.54 KB)
Anzt, H., T. Gruetzmacher, E. 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.
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., 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)
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., 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, and E. S. Quintana-Ortí, 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., and E. S. Quintana-Ortí, 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., 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-Ortí, Adaptive Precision in Block‐Jacobi Preconditioning for Iterative Sparse Linear System Solvers,” Concurrency Computation: Practice and Experience, March 2018.
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.  (1.9 MB)
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.  (1.51 MB)
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., 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)
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)
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, 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., 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)
Anzt, H., E. Chow, and J. Dongarra, Iterative Sparse Triangular Solves for Preconditioning,” EuroPar 2015, Vienna, Austria, Springer Berlin, August 2015.  (322.36 KB)
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., T. Huckle, J. Bräckle, and J. Dongarra, Incomplete Sparse Approximate Inverses for Parallel Preconditioning,” Parallel Computing, vol. 71, pp. 1–22, January 2018.
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, and J. Dongarra, On the performance and energy efficiency of sparse linear algebra on GPUs,” International Journal of High Performance Computing Applications, October 2016.
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)
Anzt, H., G. Flegar, T. Grützmacher, and E. S. Quintana-Ortí, Toward a Modular Precision Ecosystem for High-Performance Computing,” The International Journal of High Performance Computing Applications, September 2019.  (1.93 MB)
Anzt, H., and J. Dongarra, A Jaccard Weights Kernel Leveraging Independent Thread Scheduling on GPUs,” SBAC-PAD, 2018.  (237.68 KB)
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., 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)

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