Iterative sparse triangular solves for preconditioning H Anzt, E Chow, J Dongarra European Conference on Parallel Processing, 650-661, 2015 | 61 | 2015 |

Improving the performance of CA-GMRES on multicores with multiple GPUs I Yamazaki, H Anzt, S Tomov, M Hoemmen, J Dongarra 2014 IEEE 28th International Parallel and Distributed Processing Symposium …, 2014 | 43 | 2014 |

HiFlow^{3} a flexible and hardware-aware parallel finite element packageV Heuveline Proceedings of the 9th Workshop on Parallel/High-Performance Object-Oriented …, 2010 | 39 | 2010 |

Accelerating collaborative filtering using concepts from high performance computing M Gates, H Anzt, J Kurzak, J Dongarra 2015 IEEE International Conference on Big Data (Big Data), 667-676, 2015 | 37 | 2015 |

A block-asynchronous relaxation method for graphics processing units H Anzt, S Tomov, J Dongarra, V Heuveline Journal of Parallel and Distributed Computing 73 (12), 1613-1626, 2013 | 34 | 2013 |

Asynchronous iterative algorithm for computing incomplete factorizations on GPUs E Chow, H Anzt, J Dongarra International Conference on High Performance Computing, 1-16, 2015 | 32 | 2015 |

Implementation and tuning of batched Cholesky factorization and solve for NVIDIA GPUs J Kurzak, H Anzt, M Gates, J Dongarra IEEE Transactions on Parallel and Distributed Systems 27 (7), 2036-2048, 2015 | 30 | 2015 |

Implementing a Sparse Matrix Vector Product for the SELL-C/SELL-C-σ formats on NVIDIA GPUs H Anzt, S Tomov, J Dongarra University of Tennessee, Tech. Rep. ut-eecs-14-727, 2014 | 28 | 2014 |

Accelerating the LOBPCG method on GPUs using a blocked sparse matrix vector product. H Anzt, S Tomov, JJ Dongarra SpringSim (HPS), 75-82, 2015 | 27 | 2015 |

Energy efficiency of mixed precision iterative refinement methods using hybrid hardware platforms H Anzt, B Rocker, V Heuveline Computer Science-Research and Development 25 (3-4), 141-148, 2010 | 26 | 2010 |

With extreme computing, the rules have changed J Dongarra, S Tomov, P Luszczek, J Kurzak, M Gates, I Yamazaki, H Anzt, ... Computing in Science & Engineering 19 (3), 52-62, 2017 | 25 | 2017 |

Analysis and optimization of power consumption in the iterative solution of sparse linear systems on multi-core and many-core platforms H Anzt, V Heuveline, JI Aliaga, M Castillo, JC Fernandez, R Mayo, ... 2011 International Green Computing Conference and Workshops, 1-6, 2011 | 24 | 2011 |

Heterogeneous streaming CJ Newburn, G Bansal, M Wood, L Crivelli, J Planas, A Duran, P Souza, ... 2016 IEEE International Parallel and Distributed Processing Symposium …, 2016 | 23 | 2016 |

Block-asynchronous multigrid smoothers for GPU-accelerated systems H Anzt, S Tomov, M Gates, J Dongarra, V Heuveline Procedia Computer Science 9, 7-16, 2012 | 23 | 2012 |

Sparse-dense Sylvester equations in H2-model order reduction P Benner, M Køhler, J Saak | 23 | 2011 |

HiFlow^{3}: A Hardware-Aware Parallel Finite Element PackageH Anzt, W Augustin, M Baumann, T Gengenbach, T Hahn, ... Tools for High Performance Computing 2011, 139-151, 2012 | 22 | 2012 |

Adaptive precision in block‐Jacobi preconditioning for iterative sparse linear system solvers H Anzt, J Dongarra, G Flegar, NJ Higham, ES Quintana‐Ortí Concurrency and Computation: Practice and Experience 31 (6), e4460, 2019 | 20 | 2019 |

Preconditioned Krylov solvers on GPUs H Anzt, M Gates, J Dongarra, M Kreutzer, G Wellein, M Köhler Parallel Computing 68, 32-44, 2017 | 20 | 2017 |

Acceleration of GPU-based Krylov solvers via data transfer reduction H Anzt, S Tomov, P Luszczek, W Sawyer, J Dongarra The International Journal of High Performance Computing Applications 29 (3 …, 2015 | 20 | 2015 |

Incomplete sparse approximate inverses for parallel preconditioning H Anzt, TK Huckle, J Bräckle, J Dongarra Parallel Computing 71, 1-22, 2018 | 19 | 2018 |