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Author

Yosuke Oyama

Observed name: Oyama, Yosuke
Institution: Fujitsu (Japan)
ORCID: 0000-0001-8316-3291
Observed ORCID: 0000-0001-8316-3291
OpenAlex ID: A5001297674
The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism
2020 · Yosuke Oyama, Naoya Maruyama, et al. · IEEE Transactions on Parallel and Distributed Systems · 41
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Predicting statistics of asynchronous SGD parameters for a large-scale distributed deep learning system on GPU supercomputers
2016 · Yosuke Oyama, Akihiro Nomura, et al. · IEEE International Conference on Big Data (BigData) · 28
Matrix Engines for High Performance Computing: A Paragon of Performance or Grasping at Straws?
2021 · Jens Domke, Emil Vatai, et al. · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · 27
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Accelerating Deep Learning Frameworks with Micro-Batches
2018 · Yosuke Oyama, Tal Ben-Nun, et al. · IEEE International Conference on Cluster Computing (CLUSTER) · 24
Co-design Center for Exascale Machine Learning Technologies (ExaLearn)
2021 · Francis J Alexander, James Ang, et al. · The International Journal of High Performance Computing Applications · 13
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Self-Supervised Learning by Element Shuffling for Material Property Prediction
2025 · Yusuke Majima, Yosuke Oyama, et al. · Procedia Computer Science · 2
Efficient and Large Scale Pre-training Techniques for Japanese Natural Language Processing
2021 · Akihiko Kasagi, Masahiro Asaoka, et al. · International Symposium on Computing and Networking · 2
Asynchronous, Data-Parallel Deep Convolutional Neural Network Training with Linear Prediction Model for Parameter Transition
2017 · Ikuro Sato, Ryo Fujisaki, et al. · Lecture notes in computer science · 1
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LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models
2025 · Oyama, Yosuke, Majima, Yusuke, et al. · arXiv (Cornell University)
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Accelerating AlphaFold2 Inference of Protein Three-Dimensional Structure on the Supercomputer Fugaku
2023 · Yosuke Oyama, Akihiro Tabuchi, et al.
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Accelerating Hybrid DFT Simulations Using Performance Modeling on Supercomputers
2023 · Yosuke Oyama, Takumi Honda, et al. · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)
Matrix Engines for High Performance Computing: A Paragon of Performance or Grasping at Straws?
2020 · 陳, 鵬, Chen, Peng, et al. · Institutional Repositories DataBase (IRDB)
The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism
2020 · 大山 洋介, Yosuke Oyama, et al. · Institutional Repositories DataBase (IRDB)
DiHydrogen
2020 · Maruyama, Naoya, Essen, Brian, et al. · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)
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
2019 · 土川, 稔生, Tsuchikawa, Toshiki, et al. · Institutional Repositories DataBase (IRDB)
Toward Training a Large 3D Cosmological CNN with Hybrid Parallelization
2019 · 大山 洋介, Yosuke Oyama, et al. · Institutional Repositories DataBase (IRDB)
u-cuDNN: Accelerating Deep Learning Frameworks with Micro-Batches
2019 · 大山, 洋介, Oyama, Yosuke, et al. · Institutional Repositories DataBase (IRDB)
Accelerating Deep Learning Frameworks with Micro-batches
2018 · 大山, 洋介, Oyama, Yosuke, et al. · Institutional Repositories DataBase (IRDB)
機械学習による計算機トレースの自動生成
2018 · 土川, 稔生, Tsuchikawa, Toshiki, et al. · Institutional Repositories DataBase (IRDB)
μ-cuDNN
2018 · 大山, 洋介, Oyama, Yosuke, et al. · Institutional Repositories DataBase (IRDB)
深層学習におけるBatchNormalization使用時の計算時間と精度の関係性
2018 · 八島, 慶汰, Yashima, Keita, et al. · Institutional Repositories DataBase (IRDB)
μ-cuDNN: Accelerating Deep Learning Frameworks with Micro-Batching
2018 · Oyama, Yosuke, Ben-Nun, Tal, et al. · arXiv (Cornell University)
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μ-cuDNN: Accelerating Deep Learning Frameworks with Micro-Batching
2018 · 大山, 洋介, Oyama, Yosuke, et al. · Institutional Repositories DataBase (IRDB)
Less is More: Accelerating Deep Neural Networks with Micro-Batching
2017 · 大山 洋介, Yosuke Oyama, et al. · Institutional Repositories DataBase (IRDB)
Predicting Probabilistic Parameters of a Large-Scale Asynchronous SGD Deep Learning System
2017 · 大山 洋介, Yosuke Oyama, et al. · Institutional Repositories DataBase (IRDB)
Works count: 38
Citation count: 138
H-index: 5
I10-index: 5
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