RIKEN Center for Computational Science Data Management Platform Development Unit

Unit Leader: Kento Sato (Ph.D.)

Japanese Page

Research Summary

In the TRIP-AGIS project, R-CCS is operating high-performance computers and developing systems for their advancement in order to promote AI for Science research. In order to develop and utilize generative AI models (scientific foucation models) for scientific research that handle diverse data corresponding to multimodal AI models, our research unit is analyzing the performance of and developoing system software for the supercomputer "Fugaku" and AI-specific computers equipped with GPUs. We are also developing a data management infrastructure for efficient training, inference and utilization of the scientific foudation model. Furthermore, in conjunction with the automation technology developed by TRIP-AGIS, we are conducting research and development of foudamental technologies related to data to enable real-time processing of enormous amounts of diverse data. This enables high-speed training and inference cycles and aims to accelerate the development and utilization of scientific foudation models. Specifically, we are conducting the following research and development: (1) Optimization of data placement for training and inference using hierarchical memory/storage systems; (2) Research and development of high-performance and scalable fault-tolerant techniques for large-scale model training and inference; (3) Research and development of data compression techniques to improve data communication, transfer, management, model training, and inference; (4) Research and development on workflow systems to streamline model training, inference, and utilization; and (5) Other R&D to promote AI for Sciences research.

Main Research Fields

Informatics

Related Research Fields

Engineering
Complex Systems
Interdisciplinary Science & Engineering
Mathematical & Physical Sciences

Keywords

Big Data Processing Platform
Deep Learning Platform
Fault Tolerance
Performance evaluation / analysis
HPC tools

Selected Publications

1. Taiyu Wang, Qinglin Yang, Kaiming Zhu, Junbo Wang, Chunhua Su, Kento Sato,
"LDS-FL: Loss Differential Strategy based Federated Learning for Privacy Preserving,"
in IEEE Transactions on Information Forensics and Security, doi: 10.1109/TIFS.2023.3322328. , 2023
2. Satoru Hamamoto, Masaki Oura, Atsuomi Shundo, Daisuke Kawaguchi, SatoruYamamoto, Hidekazu Takano, Masayuki Uesugi, Akihisa Takeuchi, Takahiro Iwai, Yasuo Seto, Yasumasa Joti, Kento Sato, Keiji Tanaka & Takaki Hatsui
"Demonstration of efficient transfer learning in segmentation problem in synchrotron radiation X-ray CT data for epoxy resin",
Science and Technology of Advanced Materials: Methods, doi: 10.1080/27660400.2023.2270529, 2023
3. Fu Xiang, Hao Tang, Huimin Liao, Xin Huang, Wubiao Xu, Shimeng Meng, Weiping Zhang, Luanzheng Guo and Kento Sato,
"A High-dimensional Algorithm-Based Fault Tolerance Scheme"
APDCM 2023, IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), St. Petersburg, Florida USA, 2023, doi: 10.1109/IPDPSW59300.2023.00061
4. Takaaki Fukai, Kento Sato and Takahiro Hirofuchi,
"Analyzing I/O Performance of a Hierarchical HPC Storage System for Distributed Deep Learning",
The 23rd International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT’22), December, 2022, Sendai, Japan
5. Xi Zhu, Junbo Wang, Wuhui Chen, Kento Sato,
"Model compression and privacy preserving framework for federated learning",
Future Generation Computer Systems, 2022, ISSN 0167-739X, doi: 10.1016/j.future.2022.10.026
6. Amitangshu Pal, Junbo Wang, Yilang Wu, Krishna Kant, Zhi Liu, Kento Sato,
"Social Media Driven Big Data Analysis for Disaster Situation Awareness: A Tutorial",
in IEEE Transactions on Big Data, doi: 10.1109/TBDATA.2022.3158431, Mar., 2022
7. Feiyuan Liang, Qinglin Yang, Ruiqi Liu, Junbo Wang, Kento Sato, Jian Guo,
"Semi-Synchronous Federated Learning Protocol with Dynamic Aggregation in Internet of Vehicles",
in IEEE Transactions on Vehicular Technology, doi: 10.1109/TVT.2022.3148872, Feb., 2022
8. Akihiro Tabuchi, Koichi Shirahata, Masafumi Yamazaki, Akihiko Kasagi, Takumi Honda, Kouji Kurihara, Kentaro Kawakami, Tsuguchika Tabaru, Naoto Fukumoto, Akiyoshi Kuroda, Takaaki Fukai and Kento Sato,
"The 16,384-node Parallelism of 3D-CNN Training on An Arm CPU based Supercomputer",
28th IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC2021), Nov, 2021
9. Steven Farrell, Murali Emani, Jacob Balma, Lukas Drescher, Aleksandr Drozd, Andreas Fink, Geoffrey Fox, David Kanter, Thorsten Kurth, Peter Mattson, Dawei Mu, Amit Ruhela, Kento Sato,,Koichi Shirahata, Tsuguchika Tabaru, Aristeidis Tsaris, Jan Balewski, Ben Cumming, Takumi Danjo, Jens Domke, Takaaki Fukai, Naoto Fukumoto, Tatsuya Fukushi, Balazs Gerofi, Takumi Honda, Toshiyuki Imamura, Akihiko Kasagi, Kentaro Kawakami, Shuhei Kudo, Akiyoshi Kuroda, Maxime Martinasso, Satoshi Matsuoka, Kazuki Minami, Prabhat Ram, Takashi Sawada, Mallikarjun Shankar, Tom St. John, Akihiro Tabuchi, Venkatram Vishwanath, Mohamed Wahib, Masafumi Yamazaki, Junqi Yin and Henrique Mendonca,
"MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems",
The Workshop on Machine Learning in High Performance Computing Environments (MLHPC) 2021 in conjunction with SC21, Nov, 2021
10. Rupak Roy, Kento Sato, Subhadeep Bhattacharya, Xingang Fang, Yasumasa Joti, Takaki Hatsui, Toshiyuki Hiraki, Jian Guo and Weikuan Yu,
"Compression of Time Evolutionary Image Data through Predictive Deep Neural Networks",
In the proceedings of the 21 IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2021), May, 2021

Lab Members

Principal investigator

Kento Sato: Unit Leader

Core members

Ke Cui: Postdoctoral Researcher
Masaru Nagaso: Technical Scientist

Careers

Position	Deadline
Seeking Senior Technical Scientist or Technical Scientist (K24038)	Open until filled
Seeking Senior Scientist, Research Scientist or Postdoctoral Researcher (K24020)	Open until filled

Contact Information

RIKEN Center for Computational Science (R-CCS) R503
7-1-26, Minatojima-minami-machi,
Chuo-ku, Kobe,Hyogo
650-0047, Japan
Tel: +81-(0)78-940-5555
Fax: +81-(0)78-304-4956
Email: kento.sato [at] riken.jp