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Past Research Teams
High Performance Artificial Intelligence Systems Research Team (Matsuoka)
High Performance Artificial Intelligence Systems Research Team (Matsuoka)
Japanese
Team Leader Satoshi Matsuoka
- 2019
- Team Leader, High Performance Artificial Intelligence Systems Research Team, R-CCS, RIKEN (-present)
- 2018
- Director, R-CCS, RIKEN (-present)
- Specially Appointed Professor, Tokyo Tech (-present)
- 2017
- Director, Real World Big Data Computing Open Innovation Laboratory (RWBC-OIL), AIST and Tokyo Tech
- 2000
- Full Professor, Global Scientific Information and Computing Center (GSIC), the Tokyo Institute of Technology
- 1993
- Ph. D. from the University of Tokyo
Keyword
- High Performance Artificial Intelligence Systems
- Scalable Deep Learning
- Performance Modeling of AI Systems e.g. Deep Learning
- Acceleration of Advanced Deep Learning Algorithms
- Convergence of AI and Simulation
Research summary
The High Performance Artificial Intelligence Systems Research Team is an R-CCS laboratory focusing on convergence of HPC and AI, namely high performance systems, software, and algorithms research for artificial intelligence/machine learning. In collaboration with other research institutes in HPC and AI-related research in Japan as well as globally, it seeks to develop next-generation AI technology that will utilize state-of-the-art high-performance computation facilities, including Fugaku. Specifically, we conduct research on next-generation AI systems by focusing on the following topics:
- Extreme speedup and scalability of deep learning: Achieve extreme scalability of deep learning in large-scale supercomputing environments including the post-K, extending the latest algorithms and frameworks for deep learning.
- Performance analysis of deep learning: Accelerate computational kernels for AI over the state-of-the-art hardware architectures by analyzing algorithms for deep learning and other machine learning/AI, measuring their performance and constructing their performance models.
- Acceleration of modern AI algorithms: Accelerate advanced AI algorithms, such as ultra-deep neural networks and high-resolution GAN over images, those that require massive computational resources, using extreme-scale deep learning systems.
- Acceleration of HPC algorithms using machine learning: Accelerate HPC algorithms and applications using empirical models based on machine learning.
Representative papers
- Jens Domke, Emil Vatai, Aleksandr Drozd, Peng Chen, Yosuke Oyama, Lingqi Zhang, Shweta Salaria, Daichi Mukunoki, Artur Podobas, Mohamed Wahib, Satoshi Matsuoka:
"Matrix Engines for HPC: A Performance Study from the Applications Perspective",
35th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2021) - Albert Khaira, Truong Thao Nguyen, Leonardo Bautista Gomez, Ryousei Takano, Rosa Badia, Mohamed Wahib:
"An Oracle for Guiding Large-Scale Model/Hybrid Parallel Training of Convolutional Neural Networks",
30th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC 2021) - Peng Chen, Mohamed Wahib, Xiao Wang, shinichiro takizawa, Takahiro Hirofuchi, Ogawa Hirotaka, Satoshi Matsuoka:
"Performance Portable Back-projection Algorithms on CPUs: Agnostic Data Locality and Vectorization Optimizations",
35th ACM International Conference on Supercomputing (ICS 2021) - Jun Li, Minjun Li, Zhigang Cai, Francois Trahay, Mohamed Wahib, Balazs Gerofi, Zhiming Liu, Jianwei Liao:
"Intra-page Cache Update in SLC Mode with Partial Programming in High Density SSDs",
50th International Conference on Parallel Processing (ICPP 2021) - Mohamed Wahib, Haoyu Zhang, Truong Thao Nguyen, Aleksandr Drozd, Jens Domke, Lingqi Zhang, Ryousei Takano, Satoshi Matsuoka:
"Scaling Deep Learning Workloads Beyond Memory Capacity",
International Conference for High Performance Computing, Networking, Storage, and Analysis (SC 2020) - Lingqi Zhang, Wahib Mohamed, Haoyu Zhang, Matsuoka Satoshi:
"A Study of Single and Multi-device Synchronization Methods in Nvidia GPUs",
34th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2020) - Jens Domke, Satoshi Matsuoka, Ivan R. Ivanov, Yuki Tsushima, Tomoya Yuki, Akihiro Nomura, Shinichi Miura, Nic McDonald, Dennis L. Floyd, Nicolas Dube:
"The First Supercomputer with HyperX Topology: A Viable Alternative to Fat-Trees?",
International Conference for High Performance Computing, Networking, Storage, and Analysis (SC 2019) - Chen Peng,Wahib Mohamed,Takizawa Shinichiro,Matsuoka Satoshi:
"A Versatile Software Systolic Execution Model for GPU Memory Bound Kernels",
International Conference for High Performance Computing, Networking, Storage, and Analysis (SC 2019) - Jens Domke, Kazuaki Matsumura, Mohamed Wahib, Haoyu Zhang, Keita Yashima,Toshiki Tsuchikawa, Yohei Tsuji, Artur Podobas, Satoshi Matsuoka:
"Double-precision FPUs in High-Performance Computing: an Embarrassment of Riches?",
33th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2019) - Shweta Salaria, Aleksandr Drozd, Artur Podobas, Satoshi Matsuoka:
"Learning Neural Representations for Predicting GPU Performance",
ISC High Performance 2019 (ISC 2019)
