Rio Yokota
Title
Cited by
Cited by
Year
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
1572009
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
862011
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
742012
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
692010
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
622012
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
572009
An FMM based on dual tree traversal for many-core architectures
R Yokota
Journal of Algorithms & Computational Technology 7 (3), 301-324, 2013
502013
Treecode and fast multipole method for N-body simulation with CUDA
R Yokota, LA Barba
GPU Computing Gems Emerald Edition, 113-132, 2011
482011
Data‐driven execution of fast multipole methods
H Ltaief, R Yokota
Concurrency and Computation: Practice and Experience 26 (11), 1935-1946, 2014
442014
Hierarchical n-body simulations with autotuning for heterogeneous systems
R Yokota, L Barba
Computing in Science & Engineering 14 (3), 30-39, 2012
432012
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
412007
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 Conference on Computer Vision and Pattern …, 2019
35*2019
User’s Manual of CAIN
K Yokoya, P Chen
Version 2.35, 2003
312003
How will the fast multipole method fare in the exascale era
LA Barba, R Yokota
SIAM News 46 (6), 1-3, 2013
262013
Fast multipole preconditioners for sparse matrices arising from elliptic equations
H Ibeid, R Yokota, J Pestana, D Keyes
arXiv preprint arXiv:1308.3339, 2013
24*2013
Fork-join and data-driven execution models on multi-core architectures: Case study of the FMM
A Amer, N Maruyama, M Pericās, K Taura, R Yokota, S Matsuoka
International Supercomputing Conference, 255-266, 2013
242013
Practical deep learning with bayesian principles
K Osawa, S Swaroop, MEE Khan, A Jain, R Eschenhagen, RE Turner, ...
Advances in neural information processing systems, 4287-4299, 2019
232019
Communication complexity of the fast multipole method and its algebraic variants
R Yokota, G Turkiyyah, D Keyes
arXiv preprint arXiv:1406.1974, 2014
232014
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
222012
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
212013
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