Rio Yokota
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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
42 TFlops hierarchical N-body simulations on GPUs with applications in both astrophysics and turbulence
T Hamada, T Narumi, R Yokota, K Yasuoka, K Nitadori, M Taiji
Proceedings of the Conference on High Performance Computing Networking …, 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
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
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
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
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
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
An FMM based on dual tree traversal for many-core architectures
R Yokota
Journal of Algorithms & Computational Technology 7 (3), 301-324, 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
Treecode and fast multipole method for N-body simulation with CUDA
R Yokota, LA Barba
GPU Computing Gems Emerald Edition, 113-132, 2011
Hierarchical n-body simulations with autotuning for heterogeneous systems
R Yokota, L Barba
Computing in Science & Engineering 14 (3), 30-39, 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
Data‐driven execution of fast multipole methods
H Ltaief, R Yokota
Concurrency and Computation: Practice and Experience 26 (11), 1935-1946, 2014
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
How will the fast multipole method fare in the exascale era
LA Barba, R Yokota
SIAM News 46 (6), 1-3, 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
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
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
Recovering single precision accuracy from Tensor Cores while surpassing the FP32 theoretical peak performance
H Ootomo, R Yokota
The International Journal of High Performance Computing Applications 36 (4 …, 2022
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