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2020 – today
- 2022
[j15]Kazuki Osawa
, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Chuan-Sheng Foo, Rio Yokota:
Scalable and Practical Natural Gradient for Large-Scale Deep Learning. IEEE Trans. Pattern Anal. Mach. Intell. 44(1): 404-415 (2022)- [i24]Hiroyuki Ootomo, Rio Yokota:
Recovering single precision accuracy from Tensor Cores while surpassing the FP32 theoretical peak performance. CoRR abs/2203.03341 (2022) - 2021
- [d1]Tingyu Wang, Rio Yokota, Lorena A. Barba:
ExaFMM: a high-performance fast multipole method library with C++ and Python interfaces. J. Open Source Softw. 6(61): 3145 (2021) - [c24]Shun Iwase, Xingyu Liu, Rawal Khirodkar, Rio Yokota, Kris M. Kitani:
RePOSE: Fast 6D Object Pose Refinement via Deep Texture Rendering. ICCV 2021: 3283-3292 - [i23]Shun Iwase, Xingyu Liu, Rawal Khirodkar, Rio Yokota, Kris M. Kitani:
RePOSE: Real-Time Iterative Rendering and Refinement for 6D Object Pose Estimation. CoRR abs/2104.00633 (2021) - [i22]Hana Hoshino, Kei Ota, Asako Kanezaki, Rio Yokota:
OPIRL: Sample Efficient Off-Policy Inverse Reinforcement Learning via Distribution Matching. CoRR abs/2109.04307 (2021) - 2020
- [c23]Rise Ooi, Takeshi Iwashita, Takeshi Fukaya, Akihiro Ida, Rio Yokota:
Effect of Mixed Precision Computing on H-Matrix Vector Multiplication in BEM Analysis. HPC Asia 2020: 92-101 - [c22]Yuichiro Ueno, Kazuki Osawa, Yohei Tsuji, Akira Naruse, Rio Yokota:
Rich Information is Affordable: A Systematic Performance Analysis of Second-order Optimization Using K-FAC. KDD 2020: 2145-2153 - [i21]Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Chuan-Sheng Foo, Rio Yokota:
Scalable and Practical Natural Gradient for Large-Scale Deep Learning. CoRR abs/2002.06015 (2020) - [i20]Kento Doi, Ryuhei Hamaguchi, Shun Iwase, Rio Yokota, Yutaka Matsuo, Ken Sakurada:
Epipolar-Guided Deep Object Matching for Scene Change Detection. CoRR abs/2007.15540 (2020)
2010 – 2019
- 2019
- [j14]Ichitaro Yamazaki, Akihiro Ida, Rio Yokota, Jack J. Dongarra:
Distributed-memory lattice H-matrix factorization. Int. J. High Perform. Comput. Appl. 33(5) (2019) - [j13]Akihiro Ida, Hiroshi Nakashima, Tasuku Hiraishi, Ichitaro Yamazaki, Rio Yokota, Takeshi Iwashita:
QR Factorization of Block Low-rank Matrices with Weak Admissibility Condition. J. Inf. Process. 27: 831-839 (2019) - [j12]Mustafa Abdul Jabbar, Mohammed A. Al Farhan, Noha Al-Harthi, Rui Chen, Rio Yokota, Hakan Bagci, David E. Keyes:
Extreme Scale FMM-Accelerated Boundary Integral Equation Solver for Wave Scattering. SIAM J. Sci. Comput. 41(3): C245-C268 (2019) - [c21]Yuichiro Ueno, Rio Yokota:
Exhaustive Study of Hierarchical AllReduce Patterns for Large Messages Between GPUs. CCGRID 2019: 430-439 - [c20]Hiroki Naganuma, Rio Yokota:
A Performance Improvement Approach for Second-Order Optimization in Large Mini-batch Training. CCGRID 2019: 696-703 - [c19]Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Rio Yokota, Satoshi Matsuoka:
Large-Scale Distributed Second-Order Optimization Using Kronecker-Factored Approximate Curvature for Deep Convolutional Neural Networks. CVPR 2019: 12359-12367 - [c18]Yohei Tsuji, Kazuki Osawa, Yuichiro Ueno, Akira Naruse, Rio Yokota, Satoshi Matsuoka:
Performance Optimizations and Analysis of Distributed Deep Learning with Approximated Second-Order Optimization Method. ICPP Workshops 2019: 21:1-21:8 - [c17]Satoshi Ohshima, Ichitaro Yamazaki, Akihiro Ida, Rio Yokota:
Optimization of Numerous Small Dense-Matrix-Vector Multiplications in H-Matrix Arithmetic on GPU. MCSoC 2019: 9-16 - [c16]Kazuki Osawa, Siddharth Swaroop, Mohammad Emtiyaz Khan, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota:
Practical Deep Learning with Bayesian Principles. NeurIPS 2019: 4289-4301 - [i19]Kazuki Osawa, Siddharth Swaroop, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota, Mohammad Emtiyaz Khan:
Practical Deep Learning with Bayesian Principles. CoRR abs/1906.02506 (2019) - [i18]Rise Ooi, Takeshi Iwashita, Takeshi Fukaya, Akihiro Ida, Rio Yokota:
Effect of Mixed Precision Computing on H-Matrix Vector Multiplication in BEM Analysis. CoRR abs/1911.00093 (2019) - 2018
- [j11]Huda Ibeid, Rio Yokota, Jennifer Pestana, David E. Keyes:
Fast multipole preconditioners for sparse matrices arising from elliptic equations. Comput. Vis. Sci. 18(6): 213-229 (2018) - [c15]Ichitaro Yamazaki, Ahmad Abdelfattah, Akihiro Ida, Satoshi Ohshima, Stanimire Tomov, Rio Yokota, Jack J. Dongarra:
Performance of Hierarchical-matrix BiCGStab Solver on GPU Clusters. IPDPS 2018: 930-939 - [c14]Satoshi Ohshima, Ichitaro Yamazaki, Akihiro Ida, Rio Yokota:
Optimization of Hierarchical Matrix Computation on GPU. SCFA 2018: 274-292 - [e5]Rio Yokota, Weigang Wu:
Supercomputing Frontiers - 4th Asian Conference, SCFA 2018, Singapore, March 26-29, 2018, Proceedings. Lecture Notes in Computer Science 10776, Springer 2018, ISBN 978-3-319-69952-3 [contents] - [e4]Rio Yokota, Michèle Weiland, David E. Keyes, Carsten Trinitis:
High Performance Computing - 33rd International Conference, ISC High Performance 2018, Frankfurt, Germany, June 24-28, 2018, Proceedings. Lecture Notes in Computer Science 10876, Springer 2018, ISBN 978-3-319-92039-9 [contents] - [e3]Rio Yokota, Michèle Weiland, John Shalf, Sadaf R. Alam:
High Performance Computing - ISC High Performance 2018 International Workshops, Frankfurt/Main, Germany, June 28, 2018, Revised Selected Papers. Lecture Notes in Computer Science 11203, Springer 2018, ISBN 978-3-030-02464-2 [contents] - [i17]Mustafa Abdul Jabbar, Mohammed A. Al Farhan, Noha Al-Harthi, Rui Chen, Rio Yokota, Hakan Bagci, David E. Keyes:
Extreme Scale FMM-Accelerated Boundary Integral Equation Solver for Wave Scattering. CoRR abs/1803.09948 (2018) - [i16]Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Rio Yokota, Satoshi Matsuoka:
Second-order Optimization Method for Large Mini-batch: Training ResNet-50 on ImageNet in 35 Epochs. CoRR abs/1811.12019 (2018) - 2017
- [c13]Mustafa Abdul Jabbar, Mohammed A. Al Farhan, Rio Yokota, David E. Keyes:
Performance Evaluation of Computation and Communication Kernels of the Fast Multipole Method on Intel Manycore Architecture. Euro-Par 2017: 553-564 - [c12]Kazuki Osawa, Rio Yokota:
Evaluating the Compression Efficiency of the Filters in Convolutional Neural Networks. ICANN (2) 2017: 459-466 - [c11]Kazuki Osawa, Akira Sekiya, Hiroki Naganuma, Rio Yokota:
Accelerating Matrix Multiplication in Deep Learning by Using Low-Rank Approximation. HPCS 2017: 186-192 - [c10]Mustafa Abdul Jabbar, George S. Markomanolis, Huda Ibeid, Rio Yokota, David E. Keyes:
Communication Reducing Algorithms for Distributed Hierarchical N-Body Problems with Boundary Distributions. ISC 2017: 79-96 - [e2]Julian M. Kunkel, Rio Yokota, Pavan Balaji, David E. Keyes:
High Performance Computing - 32nd International Conference, ISC High Performance 2017, Frankfurt, Germany, June 18-22, 2017, Proceedings. Lecture Notes in Computer Science 10266, Springer 2017, ISBN 978-3-319-58666-3 [contents] - [e1]Julian M. Kunkel, Rio Yokota, Michela Taufer, John Shalf:
High Performance Computing - ISC High Performance 2017 International Workshops, DRBSD, ExaComm, HCPM, HPC-IODC, IWOPH, IXPUG, P^3MA, VHPC, Visualization at Scale, WOPSSS, Frankfurt, Germany, June 18-22, 2017, Revised Selected Papers. Lecture Notes in Computer Science 10524, Springer 2017, ISBN 978-3-319-67629-6 [contents] - [i15]Mustafa Abdul Jabbar, George S. Markomanolis, Huda Ibeid, Rio Yokota, David E. Keyes:
Communication Reducing Algorithms for Distributed Hierarchical N-Body Problems with Boundary Distributions. CoRR abs/1702.05459 (2017) - 2016
- [j10]Huda Ibeid, Rio Yokota, David E. Keyes:
A performance model for the communication in fast multipole methods on high-performance computing platforms. Int. J. High Perform. Comput. Appl. 30(4): 423-437 (2016) - [c9]Keisuke Fukuda, Motohiko Matsuda, Naoya Maruyama, Rio Yokota, Kenjiro Taura, Satoshi Matsuoka:
Tapas: An Implicitly Parallel Programming Framework for Hierarchical N-Body Algorithms. ICPADS 2016: 1100-1109 - [c8]Abdelhalim Amer, Satoshi Matsuoka, Miquel Pericàs, Naoya Maruyama, Kenjiro Taura, Rio Yokota, Pavan Balaji:
Scaling FMM with Data-Driven OpenMP Tasks on Multicore Architectures. IWOMP 2016: 156-170 - [i14]Rio Yokota, Huda Ibeid, David E. Keyes:
Fast Multipole Method as a Matrix-Free Hierarchical Low-Rank Approximation. CoRR abs/1602.02244 (2016) - [i13]Huda Ibeid, Rio Yokota, David E. Keyes:
A Matrix-free Preconditioner for the Helmholtz Equation based on the Fast Multipole Method. CoRR abs/1608.02461 (2016) - 2014
- [j9]Hatem Ltaief, Rio Yokota:
Data-driven execution of fast multipole methods. Concurr. Comput. Pract. Exp. 26(11): 1935-1946 (2014) - [j8]Yousuke Ohno, Rio Yokota, Hiroshi Koyama, Gentaro Morimoto, Aki Hasegawa, Gen Masumoto, Noriaki Okimoto, Yoshinori Hirano, Huda Ibeid, Tetsu Narumi, Makoto Taiji:
Petascale molecular dynamics simulation using the fast multipole method on K computer. Comput. Phys. Commun. 185(10): 2575-2585 (2014) - [j7]Rio Yokota, George Turkiyyah, David E. Keyes:
Communication Complexity of the Fast Multipole Method and its Algebraic Variants. Supercomput. Front. Innov. 1(1): 63-84 (2014) - [c7]Qi Hu, Nail A. Gumerov, Rio Yokota, Lorena A. Barba, Ramani Duraiswami:
Scalable Fast Multipole Accelerated Vortex Methods. IPDPS Workshops 2014: 966-975 - [i12]Huda Ibeid, Rio Yokota, David E. Keyes:
A Performance Model for the Communication in Fast Multipole Methods on HPC Platforms. CoRR abs/1405.6362 (2014) - [i11]Mustafa Abdul Jabbar, Rio Yokota, David E. Keyes:
Asynchronous Execution of the Fast Multipole Method Using Charm++. CoRR abs/1405.7487 (2014) - [i10]Rio Yokota, George Turkiyyah, David E. Keyes:
Communication Complexity of the Fast Multipole Method and its Algebraic Variants. CoRR abs/1406.1974 (2014) - 2013
- [j6]Rio Yokota, Lorena A. Barba, Tetsu Narumi, Kenji Yasuoka:
Petascale turbulence simulation using a highly parallel fast multipole method on GPUs. Comput. Phys. Commun. 184(3): 445-455 (2013) - [c6]Abdelhalim Amer, Naoya Maruyama, Miquel Pericàs, Kenjiro Taura, Rio Yokota, Satoshi Matsuoka:
Fork-Join and Data-Driven Execution Models on Multi-core Architectures: Case Study of the FMM. ISC 2013: 255-266 - [i9]Rio Yokota, Jennifer Pestana, Huda Ibeid, David E. Keyes:
Fast Multipole Preconditioners for Sparse Matrices Arising from Elliptic Equations. CoRR abs/1308.3339 (2013) - 2012
- [j5]Rio Yokota, Lorena A. Barba:
Hierarchical N-body Simulations with Autotuning for Heterogeneous Systems. Comput. Sci. Eng. 14(3): 30-39 (2012) - [j4]Rio Yokota, Lorena A. Barba:
A tuned and scalable fast multipole method as a preeminent algorithm for exascale systems. Int. J. High Perform. Comput. Appl. 26(4): 337-346 (2012) - [c5]Enas Yunis, Rio Yokota, Aron J. Ahmadia:
Scalable Force Directed Graph Layout Algorithms Using Fast Multipole Methods. ISPDC 2012: 180-187 - [c4]Kenjiro Taura, Jun Nakashima, Rio Yokota, Naoya Maruyama:
A Task Parallel Implementation of Fast Multipole Methods. SC Companion 2012: 617-625 - [c3]Qi Hu, Nail A. Gumerov, Rio Yokota, Lorena A. Barba, Ramani Duraiswami:
Abstract: Scalable Fast Multipole Methods for Vortex Element Methods. SC Companion 2012: 1408 - [c2]Qi Hu, Nail A. Gumerov, Rio Yokota, Lorena A. Barba, Ramani Duraiswami:
Poster: Scalable Fast Multipole Methods for Vortex Element Methods. SC Companion 2012: 1409 - [i8]Hatem Ltaief, Rio Yokota:
Data-Driven Execution of Fast Multipole Methods. CoRR abs/1203.0889 (2012) - [i7]Rio Yokota:
An FMM Based on Dual Tree Traversal for Many-core Architectures. CoRR abs/1209.3516 (2012) - 2011
- [j3]Rio Yokota, Jaydeep P. Bardhan, Matthew G. Knepley, Lorena A. Barba, Tsuyoshi Hamada:
Biomolecular electrostatics using a fast multipole BEM on up to 512 gpus and a billion unknowns. Comput. Phys. Commun. 182(6): 1272-1283 (2011) - [i6]Rio Yokota, Lorena A. Barba:
A Tuned and Scalable Fast Multipole Method as a Preeminent Algorithm for Exascale Systems. CoRR abs/1106.2176 (2011) - [i5]Rio Yokota, Tetsu Narumi, Lorena A. Barba, Kenji Yasuoka:
Petascale turbulence simulation using a highly parallel fast multipole method. CoRR abs/1106.5273 (2011) - [i4]Rio Yokota, Lorena A. Barba:
Fast N-body Simulations on GPUs. CoRR abs/1108.5815 (2011) - [i3]Rio Yokota, Lorena A. Barba:
Fast Multipole Method vs. Spectral Method for the Simulation of Isotropic Turbulence on GPUs. CoRR abs/1110.2921 (2011) - 2010
- [i2]Rio Yokota, Tsuyoshi Hamada, Jaydeep P. Bardhan, Matthew G. Knepley, Lorena A. Barba:
Biomolecular Electrostatics Simulation by an FMM-based BEM on 512 GPUs. CoRR abs/1007.4591 (2010)
2000 – 2009
- 2009
- [j2]Rio Yokota, Tetsu Narumi, Ryuji Sakamaki, Shun Kameoka, Shinnosuke Obi, Kenji Yasuoka:
Fast multipole methods on a cluster of GPUs for the meshless simulation of turbulence. Comput. Phys. Commun. 180(11): 2066-2078 (2009) - [c1]Tsuyoshi Hamada, Tetsu Narumi, Rio Yokota, Kenji Yasuoka, Keigo Nitadori, Makoto Taiji:
42 TFlops hierarchical N-body simulations on GPUs with applications in both astrophysics and turbulence. SC 2009 - [i1]Rio Yokota, Lorena A. Barba, Matthew G. Knepley:
PetRBF--A parallel O(N) algorithm for radial basis function interpolation. CoRR abs/0909.5413 (2009) - 2007
- [j1]Rio Yokota, Tarun Kumar Sheel, Shinnosuke Obi:
Calculation of isotropic turbulence using a pure Lagrangian vortex method. J. Comput. Phys. 226(2): 1589-1606 (2007)
Coauthor Index
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