Rio Yokota

According to our database, Rio Yokota authored at least 90 papers between 2007 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Natural Gradient Primal-Dual Method for Decentralized Learning.

Kenta Niwa, Hiro Ishii, Hiroshi Sawada, Akinori Fujino, Noboru Harada, Rio Yokota

IEEE Trans. Signal Inf. Process. over Networks, 2024

DGEMM on integer matrix multiplication unit.

Hiroyuki Ootomo, Katsuhisa Ozaki, Rio Yokota

Int. J. High Perform. Comput. Appl., 2024

An inherently parallel ℋ²-ULV factorization for solving dense linear systems on GPUs.

Qianxiang Ma, Rio Yokota

Int. J. High Perform. Comput. Appl., 2024

Rethinking Image Super-Resolution from Training Data Perspectives.

Go Ohtani, Ryu Tadokoro, Ryosuke Yamada, Yuki M. Asano, Iro Laina, Christian Rupprecht, Nakamasa Inoue, Rio Yokota, Hirokatsu Kataoka, Yoshimitsu Aoki

CoRR, 2024

Scaling Backwards: Minimal Synthetic Pre-training?

Ryo Nakamura, Ryu Tadokoro, Ryosuke Yamada, Yuki M. Asano, Iro Laina, Christian Rupprecht, Nakamasa Inoue, Rio Yokota, Hirokatsu Kataoka

CoRR, 2024

LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs.

Akiko Aizawa, Eiji Aramaki, Bowen Chen, Fei Cheng, Hiroyuki Deguchi, Rintaro Enomoto, Kazuki Fujii, Kensuke Fukumoto, Takuya Fukushima, Namgi Han, Yuto Harada, Chikara Hashimoto, Tatsuya Hiraoka, Shohei Hisada, Sosuke Hosokawa, Lu Jie, Keisuke Kamata, Teruhito Kanazawa, Hiroki Kanezashi, Hiroshi Kataoka, Satoru Katsumata, Daisuke Kawahara, Seiya Kawano, Atsushi Keyaki, Keisuke Kiryu, Hirokazu Kiyomaru, Takashi Kodama, Takahiro Kubo, Yohei Kuga, Ryoma Kumon, Shuhei Kurita, Sadao Kurohashi, Conglong Li, Taiki Maekawa, Hiroshi Matsuda, Yusuke Miyao, Kentaro Mizuki, Sakae Mizuki, Yugo Murawaki, Ryo Nakamura, Taishi Nakamura, Kouta Nakayama, Tomoka Nakazato, Takuro Niitsuma, Jiro Nishitoba, Yusuke Oda, Hayato Ogawa, Takumi Okamoto, Naoaki Okazaki, Yohei Oseki, Shintaro Ozaki, Koki Ryu, Rafal Rzepka, Keisuke Sakaguchi, Shota Sasaki, Satoshi Sekine, Kohei Suda, Saku Sugawara, Issa Sugiura, Hiroaki Sugiyama, Hisami Suzuki, Jun Suzuki, Toyotaro Suzumura, Kensuke Tachibana, Yu Takagi, Kyosuke Takami, Koichi Takeda, Masashi Takeshita, Masahiro Tanaka, Kenjiro Taura, Arseny Tolmachev, Nobuhiro Ueda, Zhen Wan, Shuntaro Yada, Sakiko Yahata, Yuya Yamamoto, Yusuke Yamauchi, Hitomi Yanaka, Rio Yokota, Koichiro Yoshino

CoRR, 2024

Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilities.

Kazuki Fujii, Taishi Nakamura, Mengsay Loem, Hiroki Iida, Masanari Ohi, Kakeru Hattori, Hirai Shota, Sakae Mizuki, Rio Yokota, Naoaki Okazaki

CoRR, 2024

Building a Large Japanese Web Corpus for Large Language Models.

Naoaki Okazaki, Kakeru Hattori, Hirai Shota, Hiroki Iida, Masanari Ohi, Kazuki Fujii, Taishi Nakamura, Mengsay Loem, Rio Yokota, Sakae Mizuki

CoRR, 2024

Aurora-M: The First Open Source Multilingual Language Model Red-teamed according to the U.S. Executive Order.

Taishi Nakamura, Mayank Mishra, Simone Tedeschi, Yekun Chai, Jason T. Stillerman, Felix Friedrich, Prateek Yadav, Tanmay Laud, Minh Chien Vu, Terry Yue Zhuo, Diganta Misra, Ben Bogin, Xuan-Son Vu, Marzena Karpinska, Arnav Varma Dantuluri, Wojciech Kusa, Tommaso Furlanello, Rio Yokota, Niklas Muennighoff, Suhas Pai, Tosin P. Adewumi, Veronika Laippala, Xiaozhe Yao, Adalberto Junior, Alpay Ariyak, Aleksandr Drozd, Jordan Clive, Kshitij Gupta, Liangyu Chen, Qi Sun, Ken Tsui, Noah Persaud, Nour Moustafa-Fahmy, Tianlong Chen, Mohit Bansal, Nicolo Monti, Tai Dang, Ziyang Luo, Tien-Tung Bui, Roberto Navigli, Virendra Mehta, Matthew Blumberg, Victor May, Huu Nguyen, Sampo Pyysalo

CoRR, 2024

Variational Learning is Effective for Large Deep Networks.

Yuesong Shen, Nico Daheim, Bai Cong, Peter Nickl, Gian Maria Marconi, Clement Bazan, Rio Yokota, Iryna Gurevych, Daniel Cremers, Mohammad Emtiyaz Khan, Thomas Möllenhoff

Proceedings of the Forty-first International Conference on Machine Learning, 2024

When Does Second-Order Optimization Speed Up Training?

Satoki Ishikawa, Rio Yokota

Proceedings of the Second Tiny Papers Track at ICLR 2024, 2024

Formula-Supervised Visual-Geometric Pre-training.

Ryosuke Yamada, Kensho Hara, Hirokatsu Kataoka, Koshi Makihara, Nakamasa Inoue, Rio Yokota, Yutaka Satoh

Proceedings of the Computer Vision - ECCV 2024, 2024

2023

Cache Optimization and Performance Modeling of Batched, Small, and Rectangular Matrix Multiplication on Intel, AMD, and Fujitsu Processors.

Sameer Deshmukh, Rio Yokota, George Bosilca

ACM Trans. Math. Softw., September, 2023

Empirical Study on Optimizer Selection for Out-of-Distribution Generalization.

Hiroki Naganuma, Kartik Ahuja, Shiro Takagi, Tetsuya Motokawa, Rio Yokota, Kohta Ishikawa, Ikuro Sato, Ioannis Mitliagkas

Trans. Mach. Learn. Res., 2023

Improving Continual Learning by Accurate Gradient Reconstructions of the Past.

Erik A. Daxberger, Siddharth Swaroop, Kazuki Osawa, Rio Yokota, Richard E. Turner, José Miguel Hernández-Lobato, Mohammad Emtiyaz Khan

Trans. Mach. Learn. Res., 2023

The 2023 Society for Industrial and Applied Mathematics Conference on Computational Science and Engineering.

Damian W. I. Rouson, Konrad Hinsen, Jeffrey C. Carver, Irina Tezaur, John Shalf, Rio Yokota, Anshu Dubey

Comput. Sci. Eng., 2023

Computing the k-th Eigenvalue of Symmetric H²-Matrices.

M. Ridwan Apriansyah, Rio Yokota

CoRR, 2023

O(N) distributed direct factorization of structured dense matrices using runtime systems.

Sameer Deshmukh, Qinxiang Ma, Rio Yokota, George Bosilca

CoRR, 2023

ASDL: A Unified Interface for Gradient Preconditioning in PyTorch.

Kazuki Osawa, Satoki Ishikawa, Rio Yokota, Shigang Li, Torsten Hoefler

CoRR, 2023

Quantum Circuit Simulation by SGEMM Emulation on Tensor Cores and Automatic Precision Selection.

Hiroyuki Ootomo, Hidetaka Manabe, Kenji Harada, Rio Yokota

Proceedings of the High Performance Computing - 38th International Conference, 2023

Fast Symmetric Eigenvalue Decomposition via WY Representation on Tensor Core.

Shaoshuai Zhang, Ruchi Shah, Hiroyuki Ootomo, Rio Yokota, Panruo Wu

Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, 2023

Mixed-Precision Random Projection for RandNLA on Tensor Cores.

Hiroyuki Ootomo, Rio Yokota

Proceedings of the Platform for Advanced Scientific Computing Conference, 2023

O(N) distributed direct factorization of structured dense matrices using runtime systems.

Sameer Deshmukh, Rio Yokota, George Bosilca, Qianxiang Ma

Proceedings of the 52nd International Conference on Parallel Processing, 2023

Computing the k-th Eigenvalue of Symmetric H2-Matrices.

M. Ridwan Apriansyah, Rio Yokota

Proceedings of the 52nd International Conference on Parallel Processing, 2023

SegRCDB: Semantic Segmentation via Formula-Driven Supervised Learning.

Risa Shinoda, Ryo Hayamizu, Kodai Nakashima, Nakamasa Inoue, Rio Yokota, Hirokatsu Kataoka

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Pre-training Vision Transformers with Very Limited Synthesized Images.

Ryo Nakamura, Hirokatsu Kataoka, Sora Takashima, Edgar Josafat Martinez-Noriega, Rio Yokota, Nakamasa Inoue

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Reducing shared memory footprint to leverage high throughput on Tensor Cores and its flexible API extension library.

Hiroyuki Ootomo, Rio Yokota

Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region, 2023

Towards real-time formula driven dataset feed for large scale deep learning training.

Edgar Josafat Martinez-Noriega, Rio Yokota

Proceedings of the High Performance Computing for Imaging 2023, 2023

Visual Atoms: Pre-Training Vision Transformers with Sinusoidal Waves.

Sora Takashima, Ryo Hayamizu, Nakamasa Inoue, Hirokatsu Kataoka, Rio Yokota

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Pixel-level Contrastive Learning of Driving Videos with Optical Flow.

Tomoya Takahashi, Shingo Yashima, Kohta Ishikawa, Ikuro Sato, Rio Yokota

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Parallel QR Factorization of Block Low-rank Matrices.

M. Ridwan Apriansyah, Rio Yokota

ACM Trans. Math. Softw., 2022

Scalable and Practical Natural Gradient for Large-Scale Deep Learning.

Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Chuan-Sheng Foo, Rio Yokota

IEEE Trans. Pattern Anal. Mach. Intell., 2022

Recovering single precision accuracy from Tensor Cores while surpassing the FP32 theoretical peak performance.

Hiroyuki Ootomo, Rio Yokota

Int. J. High Perform. Comput. Appl., 2022

Scalable Linear Time Dense Direct Solver for 3-D Problems without Trailing Sub-Matrix Dependencies.

Qianxiang Ma, Sameer Deshmukh, Rio Yokota

Proceedings of the SC22: International Conference for High Performance Computing, 2022

QR Factorization of Block Low-Rank Matrices on Multi-instance GPU.

Satoshi Ohshima, Akihiro Ida, Rio Yokota, Ichitaro Yamazaki

Proceedings of the Parallel and Distributed Computing, Applications and Technologies, 2022

Informative Sample-Aware Proxy for Deep Metric Learning.

Aoyu Li, Ikuro Sato, Kohta Ishikawa, Rei Kawakami, Rio Yokota

Proceedings of the 4th ACM International Conference on Multimedia in Asia, 2022

OPIRL: Sample Efficient Off-Policy Inverse Reinforcement Learning via Distribution Matching.

Hana Hoshino, Kei Ota, Asako Kanezaki, Rio Yokota

Proceedings of the 2022 International Conference on Robotics and Automation, 2022

Replacing Labeled Real-image Datasets with Auto-generated Contours.

Hirokatsu Kataoka, Ryo Hayamizu, Ryosuke Yamada, Kodai Nakashima, Sora Takashima, Xinyu Zhang, Edgar Josafat Martinez-Noriega, Nakamasa Inoue, Rio Yokota

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

ExaFMM: a high-performance fast multipole method library with C++ and Python interfaces.

Tingyu Wang, Rio Yokota, Lorena A. Barba

J. Open Source Softw., 2021

RePOSE: Real-Time Iterative Rendering and Refinement for 6D Object Pose Estimation.

Shun Iwase, Xingyu Liu, Rawal Khirodkar, Rio Yokota, Kris M. Kitani

CoRR, 2021

RePOSE: Fast 6D Object Pose Refinement via Deep Texture Rendering.

Shun Iwase, Xingyu Liu, Rawal Khirodkar, Rio Yokota, Kris M. Kitani

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020

Epipolar-Guided Deep Object Matching for Scene Change Detection.

Kento Doi, Ryuhei Hamaguchi, Shun Iwase, Rio Yokota, Yutaka Matsuo, Ken Sakurada

CoRR, 2020

Rich Information is Affordable: A Systematic Performance Analysis of Second-order Optimization Using K-FAC.

Yuichiro Ueno, Kazuki Osawa, Yohei Tsuji, Akira Naruse, Rio Yokota

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Effect of Mixed Precision Computing on H-Matrix Vector Multiplication in BEM Analysis.

Rise Ooi, Takeshi Iwashita, Takeshi Fukaya, Akihiro Ida, Rio Yokota

Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region, 2020

2019

Extreme Scale FMM-Accelerated Boundary Integral Equation Solver for Wave Scattering.

Mustafa Abdul Jabbar, Mohammed A. Al Farhan, Noha Al-Harthi, Rui Chen, Rio Yokota, Hakan Bagci, David E. Keyes

SIAM J. Sci. Comput., 2019

QR Factorization of Block Low-rank Matrices with Weak Admissibility Condition.

Akihiro Ida, Hiroshi Nakashima, Tasuku Hiraishi, Ichitaro Yamazaki, Rio Yokota, Takeshi Iwashita

J. Inf. Process., 2019

Distributed-memory lattice H-matrix factorization.

Ichitaro Yamazaki, Akihiro Ida, Rio Yokota, Jack J. Dongarra

Int. J. High Perform. Comput. Appl., 2019

Practical Deep Learning with Bayesian Principles.

Kazuki Osawa, Siddharth Swaroop, Mohammad Emtiyaz Khan, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Optimization of Numerous Small Dense-Matrix-Vector Multiplications in H-Matrix Arithmetic on GPU.

Satoshi Ohshima, Ichitaro Yamazaki, Akihiro Ida, Rio Yokota

Proceedings of the 13th IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip, 2019

Performance Optimizations and Analysis of Distributed Deep Learning with Approximated Second-Order Optimization Method.

Yohei Tsuji, Kazuki Osawa, Yuichiro Ueno, Akira Naruse, Rio Yokota, Satoshi Matsuoka

Proceedings of the 48th International Conference on Parallel Processing, 2019

Large-Scale Distributed Second-Order Optimization Using Kronecker-Factored Approximate Curvature for Deep Convolutional Neural Networks.

Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Rio Yokota, Satoshi Matsuoka

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

Exhaustive Study of Hierarchical AllReduce Patterns for Large Messages Between GPUs.

Yuichiro Ueno, Rio Yokota

Proceedings of the 19th IEEE/ACM International Symposium on Cluster, 2019

A Performance Improvement Approach for Second-Order Optimization in Large Mini-batch Training.

Hiroki Naganuma, Rio Yokota

Proceedings of the 19th IEEE/ACM International Symposium on Cluster, 2019

2018

Second-order Optimization Method for Large Mini-batch: Training ResNet-50 on ImageNet in 35 Epochs.

Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Rio Yokota, Satoshi Matsuoka

CoRR, 2018

Fast multipole preconditioners for sparse matrices arising from elliptic equations.

Huda Ibeid, Rio Yokota, Jennifer Pestana, David E. Keyes

Comput. Vis. Sci., 2018

Optimization of Hierarchical Matrix Computation on GPU.

Satoshi Ohshima, Ichitaro Yamazaki, Akihiro Ida, Rio Yokota

Proceedings of the Supercomputing Frontiers - 4th Asian Conference, 2018

Performance of Hierarchical-matrix BiCGStab Solver on GPU Clusters.

Ichitaro Yamazaki, Ahmad Abdelfattah, Akihiro Ida, Satoshi Ohshima, Stanimire Tomov, Rio Yokota, Jack J. Dongarra

Proceedings of the 2018 IEEE International Parallel and Distributed Processing Symposium, 2018

2017

Communication Reducing Algorithms for Distributed Hierarchical N-Body Problems with Boundary Distributions.

Mustafa Abdul Jabbar, George S. Markomanolis, Huda Ibeid, Rio Yokota, David E. Keyes

Proceedings of the High Performance Computing - 32nd International Conference, 2017

Accelerating Matrix Multiplication in Deep Learning by Using Low-Rank Approximation.

Kazuki Osawa, Akira Sekiya, Hiroki Naganuma, Rio Yokota

Proceedings of the 2017 International Conference on High Performance Computing & Simulation, 2017

Evaluating the Compression Efficiency of the Filters in Convolutional Neural Networks.

Kazuki Osawa, Rio Yokota

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2017, 2017

Performance Evaluation of Computation and Communication Kernels of the Fast Multipole Method on Intel Manycore Architecture.

Mustafa Abdul Jabbar, Mohammed A. Al Farhan, Rio Yokota, David E. Keyes

Proceedings of the Euro-Par 2017: Parallel Processing - 23rd International Conference on Parallel and Distributed Computing, Santiago de Compostela, Spain, August 28, 2017

2016

A performance model for the communication in fast multipole methods on high-performance computing platforms.

Huda Ibeid, Rio Yokota, David E. Keyes

Int. J. High Perform. Comput. Appl., 2016

Fast Multipole Method as a Matrix-Free Hierarchical Low-Rank Approximation.

Rio Yokota, Huda Ibeid, David E. Keyes

CoRR, 2016

A Matrix-free Preconditioner for the Helmholtz Equation based on the Fast Multipole Method.

Huda Ibeid, Rio Yokota, David E. Keyes

CoRR, 2016

Scaling FMM with Data-Driven OpenMP Tasks on Multicore Architectures.

Abdelhalim Amer, Satoshi Matsuoka, Miquel Pericàs, Naoya Maruyama, Kenjiro Taura, Rio Yokota, Pavan Balaji

Proceedings of the OpenMP: Memory, Devices, and Tasks, 2016

Tapas: An Implicitly Parallel Programming Framework for Hierarchical N-Body Algorithms.

Keisuke Fukuda, Motohiko Matsuda, Naoya Maruyama, Rio Yokota, Kenjiro Taura, Satoshi Matsuoka

Proceedings of the 22nd IEEE International Conference on Parallel and Distributed Systems, 2016

2014

Communication Complexity of the Fast Multipole Method and its Algebraic Variants.

Rio Yokota, George Turkiyyah, David E. Keyes

Supercomput. Front. Innov., 2014

Petascale molecular dynamics simulation using the fast multipole method on K computer.

Yousuke Ohno, Rio Yokota, Hiroshi Koyama, Gentaro Morimoto, Aki Hasegawa, Gen Masumoto, Noriaki Okimoto, Yoshinori Hirano, Huda Ibeid, Tetsu Narumi, Makoto Taiji

Comput. Phys. Commun., 2014

A Performance Model for the Communication in Fast Multipole Methods on HPC Platforms.

Huda Ibeid, Rio Yokota, David E. Keyes

CoRR, 2014

Asynchronous Execution of the Fast Multipole Method Using Charm++.

Mustafa Abdul Jabbar, Rio Yokota, David E. Keyes

CoRR, 2014

Data-driven execution of fast multipole methods.

Hatem Ltaief, Rio Yokota

Concurr. Comput. Pract. Exp., 2014

Scalable Fast Multipole Accelerated Vortex Methods.

Qi Hu, Nail A. Gumerov, Rio Yokota, Lorena A. Barba, Ramani Duraiswami

Proceedings of the 2014 IEEE International Parallel & Distributed Processing Symposium Workshops, 2014

2013

Petascale turbulence simulation using a highly parallel fast multipole method on GPUs.

Rio Yokota, Lorena A. Barba, Tetsu Narumi, Kenji Yasuoka

Comput. Phys. Commun., 2013

Fork-Join and Data-Driven Execution Models on Multi-core Architectures: Case Study of the FMM.

Abdelhalim Amer, Naoya Maruyama, Miquel Pericàs, Kenjiro Taura, Rio Yokota, Satoshi Matsuoka

Proceedings of the Supercomputing - 28th International Supercomputing Conference, 2013

2012

A tuned and scalable fast multipole method as a preeminent algorithm for exascale systems.

Rio Yokota, Lorena A. Barba

Int. J. High Perform. Comput. Appl., 2012

Hierarchical N-body Simulations with Autotuning for Heterogeneous Systems.

Rio Yokota, Lorena A. Barba

Comput. Sci. Eng., 2012

An FMM Based on Dual Tree Traversal for Many-core Architectures

Rio Yokota

CoRR, 2012

A Task Parallel Implementation of Fast Multipole Methods.

Kenjiro Taura, Jun Nakashima, Rio Yokota, Naoya Maruyama

Proceedings of the 2012 SC Companion: High Performance Computing, 2012

Poster: Scalable Fast Multipole Methods for Vortex Element Methods.

Qi Hu, Nail A. Gumerov, Rio Yokota, Lorena A. Barba, Ramani Duraiswami

Proceedings of the 2012 SC Companion: High Performance Computing, 2012

Abstract: Scalable Fast Multipole Methods for Vortex Element Methods.

Qi Hu, Nail A. Gumerov, Rio Yokota, Lorena A. Barba, Ramani Duraiswami

Proceedings of the 2012 SC Companion: High Performance Computing, 2012

Scalable Force Directed Graph Layout Algorithms Using Fast Multipole Methods.

Enas Yunis, Rio Yokota, Aron J. Ahmadia

Proceedings of the 11th International Symposium on Parallel and Distributed Computing, 2012

2011

Biomolecular electrostatics using a fast multipole BEM on up to 512 gpus and a billion unknowns.

Rio Yokota, Jaydeep P. Bardhan, Matthew G. Knepley, Lorena A. Barba, Tsuyoshi Hamada

Comput. Phys. Commun., 2011

Fast Multipole Method vs. Spectral Method for the Simulation of Isotropic Turbulence on GPUs

Rio Yokota, Lorena A. Barba

CoRR, 2011

Fast N-body Simulations on GPUs

Rio Yokota, Lorena A. Barba

CoRR, 2011

Petascale turbulence simulation using a highly parallel fast multipole method

Rio Yokota, Tetsu Narumi, Lorena A. Barba, Kenji Yasuoka

CoRR, 2011

2010

Biomolecular Electrostatics Simulation by an FMM-based BEM on 512 GPUs

Rio Yokota, Tsuyoshi Hamada, Jaydeep P. Bardhan, Matthew G. Knepley, Lorena A. Barba

CoRR, 2010

2009

Fast multipole methods on a cluster of GPUs for the meshless simulation of turbulence.

Rio Yokota, Tetsu Narumi, Ryuji Sakamaki, Shun Kameoka, Shinnosuke Obi, Kenji Yasuoka

Comput. Phys. Commun., 2009

PetRBF--A parallel O(N) algorithm for radial basis function interpolation

Rio Yokota, Lorena A. Barba, Matthew G. Knepley

CoRR, 2009

42 TFlops hierarchical <i>N</i>-body simulations on GPUs with applications in both astrophysics and turbulence.

Tsuyoshi Hamada, Tetsu Narumi, Rio Yokota, Kenji Yasuoka, Keigo Nitadori, Makoto Taiji

Proceedings of the ACM/IEEE Conference on High Performance Computing, 2009

2007

Calculation of isotropic turbulence using a pure Lagrangian vortex method.

Rio Yokota, Tarun Kumar Sheel, Shinnosuke Obi

J. Comput. Phys., 2007