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RIKEN Center for Computational Science High Performance Big Data Research Team

Team Leader: Kento Sato (Ph.D.)

Research Summary

Kento Sato(Ph.D.)

The High-Performance Big Data Research Team at the RIKEN Center for Computational Science has be developing system software for the advancement of high-performance computers such as the Fugaku supercomputer. In particular, we aim to integrate high performance computing (HPC), big data (Big Data), and artificial intelligence (AI). To achieve this goal, we are researching and developing fundamental technologies that are universally required for the advancement of high-performance computing. Especially, we develop system software for accelerating big data processing and AI training & inference (i.e., HPC for Big Data/AI) while we also make use of big data and AI technologies for the advancement of high-performance computing (i.e., Big Data/AI for HPC). We also study technologies for designing future high-performance computers. Specifically, we are developing technologies for scalable parallel I/O, scalable machine learning and deep learning using hierarchical memory storage technology, utilizing non-volatile memory, scalable and efficient fault-tolerant technology, efficient data compression and transfer on high-speed networks, and advanced programming environments. We also explore architectures to develop the next generation of large-scale systems. We are actively collaborating with researchers from domestic and foreign companies, universities, and national laboratories to establish a high-performance big data processing infrastructure.

Main Research Fields

  • Informatics

Keywords

  • High performance computing
  • Computer System
  • Big data
  • massively parallel I/O
  • storage

Selected Publications

  • 1.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 proceedings of the 21 IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2021), (2021).
  • 2.J. Domke, E. Vatai, A. Drozd, P. Chen, Y. Oyama, L. Zhang, S. Salaria, D. Mukunoki, A. Podobas, M. Wahib, S. Matsuoka.:
    "Matrix Engines for High Performance Computing: A Paragon of Performance or Grasping at Straws?"
    In proceedings of the 35th IEEE International Parallel & Distributed Processing Symposium (IPDPS), (Portland, Oregon, USA), IEEE Computer Society, (2021).
  • 3.Tonmoy Dey, Kento Sato, Bogdan Nicolae, Jian Guo, Jens Domke, Weikuan Yu, Franck Cappello, and Kathryn Mohror.:
    "Optimizing Asynchronous Multi-Level Checkpoint/Restart Configurations with Machine Learning"
    The IEEE International Workshop on High-Performance Storage, (2020).
  • 4.M. Besta, J. Domke, M. Schneider, M. Konieczny, S.D. Girolamo, T. Schneider, A. Singla, T. Hoefler.:
    "High-Performance Routing with Multipathing and Path Diversity in Supercomputers and Data Centers"
    Accepted at the IEEE Transactions on Parallel and Distributed Systems (TPDS).
  • 5.M. Wahib, H. Zhang, T.T. Nguyen, A. Drozd, J. Domke, L. Zhang, R. Takano, S. Matsuoka.:
    "Scaling Distributed Deep Learning Workloads beyond the Memory Capacity with KARMA"
    In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC ’20, (Piscataway, NJ, USA), IEEE Press, (2020).
  • 6.Chapp, D., Rorabaugh, D., Sato, K., Ahn, D. H., & Taufer, M.:
    "A three-phase workflow for general and expressive representations of nondeterminism in HPC applications"
    The International Journal of High Performance Computing Applications, 33(6), 1175–1184. (2019).
  • 7.Chapp, D., Rorabaugh, D., Sato, K., Ahn, D. H., & Taufer, M.:
    "A three-phase workflow for general and expressive representations of nondeterminism in HPC applications"
    The International Journal of High Performance Computing Applications, 33(6), 1175–1184. (2019).
  • 8.J. Domke, S. Matsuoka, I.R. Ivanov, Y. Tsushima, T. Yuki, A. Nomura, S. Miura, N. McDonald, D.L. Floyd, N. Dube.:
    "HyperX Topology: First at-scale Implementation and Comparison to the Fat-Tree"
    Accepted at the International Conference for High Performance Computing, Networking, Storage and Analysis, SC ’19, (Piscataway, NJ, USA), IEEE Press, (2019).
  • 9.J. Domke, S. Matsuoka, I.R. Ivanov, Y. Tsushima, T. Yuki, A. Nomura, S. Miura, N. McDonald, D.L. Floyd, N. Dube.:
    "The First Supercomputer with HyperX Topology: A Viable Alternative to Fat-Trees?"
    Peer-reviewed short paper presented at the 2019 IEEE 26th Symposium on High-Performance Interconnects (HOTI 26), (2019).
  • 10.J. Domke, K. Matsumura, M. Wahib, H. Zhang, K. Yashima, T. Tsuchikawa, Y. Tsuji, A. Podobas, S. Matsuoka.:
    "Double-precision FPUs in High-Performance Computing: an Embarrassment of Riches?"
    In Proceedings of the 33th IEEE International Parallel & Distributed Processing Symposium (IPDPS), (Rio de Janeiro, Brazil), IEEE Computer Society, (2019).

Related Links

Lab Members

Principal investigator

Kento Sato
Team Leader

Core members

Andres Xavier Rubio Proano
Postdoctoral Researcher

Careers

Position Deadline
Seeking Postdoctoral Researcher or Research Scientist (R-CCS2104) 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

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