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 | 216 | 2019 |

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 | 184 | 2009 |

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 | 135* | 2019 |

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 | 102 | 2011 |

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 | 99 | 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 | 88 | 2010 |

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 | 87 | 2012 |

An FMM based on dual tree traversal for many-core architectures R Yokota Journal of Algorithms & Computational Technology 7 (3), 301-324, 2013 | 78 | 2013 |

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 | 78 | 2009 |

Treecode and fast multipole method for N-body simulation with CUDA R Yokota, LA Barba GPU Computing Gems Emerald Edition, 113-132, 2011 | 64 | 2011 |

Hierarchical n-body simulations with autotuning for heterogeneous systems R Yokota, L Barba Computing in Science & Engineering 14 (3), 30-39, 2012 | 56 | 2012 |

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 | 50 | 2007 |

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

Repose: Fast 6d object pose refinement via deep texture rendering S Iwase, X Liu, R Khirodkar, R Yokota, KM Kitani Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 44 | 2021 |

How will the fast multipole method fare in the exascale era LA Barba, R Yokota SIAM News 46 (6), 1-3, 2013 | 37 | 2013 |

Scalable and practical natural gradient for large-scale deep learning K Osawa, Y Tsuji, Y Ueno, A Naruse, CS Foo, R Yokota IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (1), 404-415, 2020 | 35 | 2020 |

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 | 33 | 2013 |

Exhaustive study of hierarchical allreduce patterns for large messages between gpus Y Ueno, R Yokota 2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid …, 2019 | 32 | 2019 |

Extreme scale FMM-accelerated boundary integral equation solver for wave scattering M Abduljabbar, MA Farhan, N Al-Harthi, R Chen, R Yokota, H Bagci, ... SIAM Journal on Scientific Computing 41 (3), C245-C268, 2019 | 31 | 2019 |

Fast multipole method as a matrix-free hierarchical low-rank approximation R Yokota, H Ibeid, D Keyes Eigenvalue Problems: Algorithms, Software and Applications in Petascale …, 2017 | 29 | 2017 |