Publications

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Agullo, E., J. Demmel, J. Dongarra, B. Hadri, J. Kurzak, J. Langou, H. Ltaeif, P. Luszczek, and S. Tomov, Numerical Linear Algebra on Emerging Architectures: The PLASMA and MAGMA Projects,” Journal of Physics: Conference Series, vol. 180, 00 2009.  (119.37 KB)
Agullo, E., C. Augonnet, J. Dongarra, H. Ltaeif, R. Namyst, S. Thibault, and S. Tomov, A Hybridization Methodology for High-Performance Linear Algebra Software for GPUs,” in GPU Computing Gems, Jade Edition, vol. 2: Elsevier, pp. 473-484, 00 2011.
Agullo, E., L. Giraud, A. Guermouche, A. Haidar, and J. Roman, Towards a Complexity Analysis of Sparse Hybrid Linear Solvers,” PARA 2010, Reykjavik, Iceland, June 2010.
Agullo, E., C. Augonnet, J. Dongarra, H. Ltaeif, R. Namyst, R. Nath, J. Roman, S. Thibault, and S. Tomov, Scheduling Cholesky Factorization on Multicore Architectures with GPU Accelerators , Knoxville, TN, 2010 Symposium on Application Accelerators in High-Performance Computing (SAAHPC'10), Poster, July 2010.  (3.86 MB)
Ahrens, J., C. M. Biwer, A. Costan, G. Antoniu, M. S. Pérez, N. Stojanovic, R. Badia, O. Beckstein, G. Fox, S. Jha, et al., A Collection of White Papers from the BDEC2 Workshop in Bloomington, IN,” Innovative Computing Laboratory Technical Report, no. ICL-UT-18-15: University of Tennessee, Knoxville, November 2018.  (9.26 MB)
Alam, S., R. F. Barrett, H. Jagode, J. A.. Kuehn, S. W. Poole, and R.. Sankaran, Impact of Quad-core Cray XT4 System and Software Stack on Scientific Computation,” Euro-Par 2009, Lecture Notes in Computer Science, vol. 5704/2009, Delft, The Netherlands, Springer Berlin / Heidelberg, pp. 334-344, August 2009.  (312.74 KB)
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. DOI: 10.1002/cpe.3341  (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.
Altintas, I., K. Marcus, V. Vural, S. Purawat, D. Crawl, G. Antoniu, A. Costan, O. Marcu, P. Balaprakash, R. Cao, et al., A Collection of White Papers from the BDEC2 Workshop in San Diego, CA,” Innovative Computing Laboratory Technical Report, no. ICL-UT-19-13: University of Tennessee, October 2019.  (8.25 MB)
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)
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)
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), Malaga, Spain, January 2005.  (72.27 KB)
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)
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. 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.
Antoniu, G., A. Costan, O. Marcu, M. S. Pérez, N. Stojanovic, R. M. Badia, M. Vázquez, S. Girona, M. Beck, T. Moore, et al., A Collection of White Papers from the BDEC2 Workshop in Poznan, Poland,” Innovative Computing Laboratory Technical Report, no. ICL-UT-19-10: University of Tennessee, Knoxville, May 2019.  (5.82 MB)
Anzt, H., S. Tomov, and J. Dongarra, Accelerating the LOBPCG method on GPUs using a blocked Sparse Matrix Vector Product,” Spring Simulation Multi-Conference 2015 (SpringSim'15), Alexandria, VA, SCS, April 2015.  (1.46 MB)
Anzt, H., W. Sawyer, S. Tomov, P. Luszczek, and J. Dongarra, Acceleration of GPU-based Krylov solvers via Data Transfer Reduction,” International Journal of High Performance Computing Applications, 2015.
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. 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., T. Huckle, J. Bräckle, and J. Dongarra, Incomplete Sparse Approximate Inverses for Parallel Preconditioning,” Parallel Computing, vol. 71, pp. 1–22, January 2018. DOI: 10.1016/j.parco.2017.10.003  (1.24 MB)
Anzt, H., J. Dongarra, and E. S. Quintana-Orti, Fine-grained Bit-Flip Protection for Relaxation Methods,” Journal of Computational Science, November 2016. DOI: 10.1016/j.jocs.2016.11.013  (1.47 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)
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., S. Tomov, J. Dongarra, and V. Heuveline, Weighted Block-Asynchronous Iteration on GPU-Accelerated Systems,” Tenth International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Platforms (Best Paper), Rhodes Island, Greece, August 2012.  (764.02 KB)
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. DOI: 10.1002/cpe.3516  (1.99 MB)
Anzt, H., J. Dongarra, M. Kreutzer, G. Wellein, and M. Kohler, Efficiency of General Krylov Methods on GPUs – An Experimental Study,” The Sixth International Workshop on Accelerators and Hybrid Exascale Systems (AsHES), Chicago, IL, IEEE, May 2016. DOI: 10.1109/IPDPSW.2016.45  (285.28 KB)
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)
Anzt, H., M. Baboulin, J. Dongarra, Y. Fournier, F. Hulsemann, A. Khabou, and Y. Wang, Accelerating the Conjugate Gradient Algorithm with GPU in CFD Simulations,” VECPAR, 2016.
Anzt, H., E. Ponce, G. D. Peterson, and J. Dongarra, GPU-accelerated Co-design of Induced Dimension Reduction: Algorithmic Fusion and Kernel Overlap,” 2nd International Workshop on Hardware-Software Co-Design for High Performance Computing, Austin, TX, ACM, November 2015.  (1.46 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., 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., 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. Chow, and J. Dongarra, On block-asynchronous execution on GPUs,” LAPACK Working Note, no. 291, November 2016.  (1.05 MB)
Anzt, H., G. Collins, J. Dongarra, G. Flegar, and E. S. Quintana-Orti, Flexible Batched Sparse Matrix-Vector Product on GPUs,” 8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA '17), Denver, CO, ACM Press, November 2017. DOI: http://dx.doi.org/10.1145/3148226.3148230  (583.4 KB)
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. DOI: 10.1177/1094342016646844  (2.08 MB)
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)
Anzt, H., and J. Dongarra, A Jaccard Weights Kernel Leveraging Independent Thread Scheduling on GPUs,” SBAC-PAD, Lyon, France, IEEE, 2018.  (237.68 KB)
Anzt, H., J. Dongarra, G. Flegar, and E. S. Quintana-Orti, 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)
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. 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. DOI: 10.1109/IPDPSW.2016.45
Anzt, H., J. Dongarra, and E. S. Quintana-Orti, 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. DOI: 10.1002/cpe.3516  (1.98 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. DOI: 10.1137/16M1079506  (19.26 MB)
Anzt, H., M. Gates, J. Dongarra, M. Kreutzer, G. Wellein, and M. Kohler, Preconditioned Krylov Solvers on GPUs,” Parallel Computing, June 2017. DOI: 10.1016/j.parco.2017.05.006  (1.19 MB)

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