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Yosuke Oyama
Yosuke Oyama
Verified email at fujitsu.com - Homepage
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Year
The case for strong scaling in deep learning: Training large 3d cnns with hybrid parallelism
Y Oyama, N Maruyama, N Dryden, E McCarthy, P Harrington, J Balewski, ...
IEEE Transactions on Parallel and Distributed Systems 32 (7), 1641-1652, 2020
422020
Matrix engines for high performance computing: A paragon of performance or grasping at straws?
J Domke, E Vatai, A Drozd, P ChenT, Y Oyama, L Zhang, S Salaria, ...
2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS …, 2021
332021
Predicting statistics of asynchronous SGD parameters for a large-scale distributed deep learning system on GPU supercomputers
Y Oyama, A Nomura, I Sato, H Nishimura, Y Tamatsu, S Matsuoka
2016 IEEE International Conference on Big Data (Big Data), 66-75, 2016
282016
Accelerating Deep Learning Frameworks with Micro-batches
Y Oyama, T Ben-Nun, T Hoefler, S Matsuoka
IEEE Cluster 2018, 0
26*
Co-design center for exascale machine learning technologies (exalearn)
FJ Alexander, J Ang, JA Bilbrey, J Balewski, T Casey, R Chard, J Choi, ...
The International Journal of High Performance Computing Applications 35 (6 …, 2021
112021
Prediction apparatus, prediction method, and prediction program
H Nishimura, S Matsuoka, A Nomura, Y Oyama, I Sato
US Patent App. 15/439,304, 2018
52018
Learning system and learning method
I Sato, R Fujisaki, A Nomura, Y Oyama, S Matsuoka
US Patent 11,521,057, 2022
32022
Toward Training a Large 3D Cosmological CNN with Hybrid Parallelization
Y Oyama, N Maruyama, N Dryden, P Harrington, J Balewski, S Matsuoka, ...
並列/分散/協調処理に関するサマーワークショップ (SWoPP2019), 2019
22019
Efficient and Large Scale Pre-training Techniques for Japanese Natural Language Processing
A Kasagi, M Asaoka, A Tabuchi, Y Oyama, T Honda, Y Sakai, T Dang, ...
2021 Ninth International Symposium on Computing and Networking (CANDAR), 108-113, 2021
12021
Dihydrogen
N Maruyama, BV Essen, NJ Dryden, TR Benson, TY Moon, Y Oyama
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States), 2020
12020
μ-cuDNN: Accelerating Deep Learning Frameworks with Micro-Batching
Y Oyama, T Ben-Nun, T Hoefler, S Matsuoka
12018
Asynchronous, data-parallel deep convolutional neural network training with linear prediction model for parameter transition
I Sato, R Fujisaki, Y Oyama, A Nomura, S Matsuoka
Neural Information Processing: 24th International Conference, ICONIP 2017 …, 2017
12017
Accelerating AlphaFold2 Inference of Protein Three-Dimensional Structure on the Supercomputer Fugaku
Y Oyama, A Tabuchi, A Tokuhisa
Proceedings of the 13th Workshop on AI and Scientific Computing at Scale …, 2023
2023
Accelerating Hybrid DFT Simulations Using Performance Modeling on Supercomputers
Y Oyama, T Honda, A Ishikawa, K Shirahata
2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet …, 2023
2023
「富岳」 における密度汎関数法計算ソフトウェア CP2K の高速化
Y OYAMA, T HONDA, K SHIRAHATA
情報処理学会研究報告 (Web) 2022 (HPC-185), 2022
2022
メモリアクセスデータを用いた機械学習によるアプリケーションの類型化
土川稔生, 遠藤敏夫, 大山洋介, 野村哲弘, 近藤正章, 松岡聡
研究報告ハイパフォーマンスコンピューティング (HPC) 2019 (12), 1-7, 2019
2019
深層学習における BatchNormalization 使用時の計算時間と精度の関係性
八島慶汰, 大山洋介, 松岡聡
研究報告ハイパフォーマンスコンピューティング (HPC) 2018 (1), 1-6, 2018
2018
機械学習による計算機トレースの自動生成
土川稔生, 大山洋介, 野村哲弘, 松岡聡
研究報告ハイパフォーマンスコンピューティング (HPC) 2018 (28), 1-6, 2018
2018
Less is More: Accelerating Deep Neural Networks with Micro-Batching
Y Oyama, T Ben-Nun, T Hoefler, S Matsuoka
情報処理学会研究報告, 2017
2017
ディープラーニングのデータ並列学習における少精度浮動小数点数を用いた通信量の削減
大山洋介, 野村哲弘, 佐藤育郎, 松岡聡
情報処理学会研究報告, 2017
2017
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