Rio Yokota
Hivatkozott rá
Hivatkozott rá
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
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
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
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
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
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
An FMM based on dual tree traversal for many-core architectures
R Yokota
Journal of Algorithms & Computational Technology 7 (3), 301-324, 2013
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
How will the fast multipole method fare in the exascale era
LA Barba, R Yokota
SIAM News 46 (6), 1-3, 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
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
Fast multipole preconditioners for sparse matrices arising from elliptic equations
H Ibeid, R Yokota, J Pestana, D Keyes
arXiv preprint arXiv:1308.3339, 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
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
Supercomputing: 28th International Supercomputing Conference, ISC 2013 …, 2013
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
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Cikkek 1–20