42 TFlops hierarchical *N*-body simulations on GPUs with applications in both astrophysics and turbulenceT Hamada, T Narumi, R Yokota, K Yasuoka, K Nitadori, M Taiji Proceedings of the Conference on High Performance Computing Networking …, 2009 | 177 | 2009 |

Practical deep learning with Bayesian principles K Osawa, S Swaroop, MEE Khan, A Jain, R Eschenhagen, RE Turner, ... Advances in neural information processing systems 32, 2019 | 159 | 2019 |

Large-scale distributed second-order optimization using kronecker-factored approximate curvature for deep convolutional neural networks K Osawa, Y Tsuji, Y Ueno, A Naruse, R Yokota, S Matsuoka Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 108* | 2019 |

Petascale turbulence simulation using a highly parallel fast multipole method on GPUs R Yokota, LA Barba, T Narumi, K Yasuoka Computer Physics Communications 184 (3), 445--455, 2012 | 98 | 2012 |

Biomolecular electrostatics using a fast multipole BEM on up to 512 GPUs and a billion unknowns R Yokota, JP Bardhan, MG Knepley, LA Barba, T Hamada Computer Physics Communications 182 (6), 1272-1283, 2011 | 94 | 2011 |

A tuned and scalable fast multipole method as a preeminent algorithm for exascale systems R Yokota, LA Barba The International Journal of High Performance Computing Applications 26 (4 …, 2012 | 86 | 2012 |

PetRBF—A parallel O (N) algorithm for radial basis function interpolation with Gaussians R Yokota, LA Barba, MG Knepley Computer Methods in Applied Mechanics and Engineering 199 (25-28), 1793-1804, 2010 | 79 | 2010 |

Fast multipole methods on a cluster of GPUs for the meshless simulation of turbulence R Yokota, T Narumi, R Sakamaki, S Kameoka, S Obi, K Yasuoka Computer Physics Communications 180 (11), 2066-2078, 2009 | 72 | 2009 |

An FMM based on dual tree traversal for many-core architectures R Yokota Journal of Algorithms & Computational Technology 7 (3), 301-324, 2013 | 70 | 2013 |

Treecode and fast multipole method for N-body simulation with CUDA R Yokota, LA Barba GPU Computing Gems Emerald Edition, 113-132, 2011 | 62 | 2011 |

Hierarchical n-body simulations with autotuning for heterogeneous systems R Yokota, L Barba Computing in Science & Engineering 14 (3), 30-39, 2012 | 54 | 2012 |

Data‐driven execution of fast multipole methods H Ltaief, R Yokota Concurrency and Computation: Practice and Experience 26 (11), 1935-1946, 2014 | 47 | 2014 |

Calculation of isotropic turbulence using a pure Lagrangian vortex method R Yokota, TK Sheel, S Obi Journal of Computational Physics 226 (2), 1589-1606, 2007 | 46 | 2007 |

How will the fast multipole method fare in the exascale era LA Barba, R Yokota SIAM News 46 (6), 1-3, 2013 | 35 | 2013 |

FMM-based vortex method for simulation of isotropic turbulence on GPUs, compared with a spectral method R Yokota, LA Barba Computers & Fluids 80, 17-27, 2013 | 30 | 2013 |

Fast multipole preconditioners for sparse matrices arising from elliptic equations H Ibeid, R Yokota, J Pestana, D Keyes Computing and Visualization in Science 18 (6), 213-229, 2018 | 27* | 2018 |

Fork-join and data-driven execution models on multi-core architectures: Case study of the FMM A Amer, N Maruyama, M Pericàs, K Taura, R Yokota, S Matsuoka International Supercomputing Conference, 255-266, 2013 | 27 | 2013 |

A task parallel implementation of fast multipole methods K Taura, J Nakashima, R Yokota, N Maruyama 2012 SC Companion: High Performance Computing, Networking Storage and …, 2012 | 27 | 2012 |

Multi-level restricted maximum likelihood covariance estimation and kriging for large non-gridded spatial datasets JE Castrillon-Candás, MG Genton, R Yokota Spatial Statistics 18, 105-124, 2016 | 25 | 2016 |

Petascale molecular dynamics simulation using the fast multipole method on K computer Y Ohno, R Yokota, H Koyama, G Morimoto, A Hasegawa, G Masumoto, ... Computer Physics Communications 185 (10), 2575-2585, 2014 | 25 | 2014 |