Centers & Labs

RIKEN Center for Computational Science

High Performance Big Data Research Team

Team Leader: Kento Sato (Ph.D.)
Kento  Sato(Ph.D.)

The High Performance Big Data Research Team is investigating and developing software to facilitate extreme-scale big data processing, machine learning and deep learning for the K computer, post-K computer and beyond. The computational power in high performance computing (HPC) systems has been dramatically increasing, driven in particular by advanced multi/many-core architectures and new memory technologies such as high bandwidth memory and hybrid memory cubes. Although these HPC systems are keeping pace with required computational and memory performance for running scientific applications, they are inadequate with respect to I/O performance required by data-intensive applications.

To resolve this I/O problem in extreme-scale supercomputers, our research team is developing system software that facilitates a variety of big data processing by taking advantage of next-generation memory and storage architectures. We especially focus on several areas: fast and scalable parallel I/O for big data processing; scalable algorithms for machine learning and deep learning on hieratical memory and storage architectures; scalable checkpoints/restarts for fault tolerance; fast data transfer techniques for multi-petabytes of big data on high-speed networks; integration of software stacks of big data for HPC; and virtualization and container technologies.

We will proactively collaborate with domestic and international researchers from private companies, academia and national laboratories. With the momentum gained through these collaborations, we will strengthen our international presence in extreme-scale big data processing.

Main Research Field

Informatics

High Performance Computing / Computer System / Software

Keywords

  • Big Data Processing Platform
  • Machine Learning/Deep Learning Platform
  • Fault Tolerance
  • File system
  • Virtualization and container technologies

Selected Publications

Papers with an asterisk(*) are based on research conducted outside of RIKEN.
  1. Xu, T., Sato, K., and Matsuoka, S.:
    “Explorations of Data Swapping on Burst Buffer”
    The 24th IEEE International Conference on Parallel and Distributed Systems (ICPADS 2018), Sentosa, Singapore. (2018).
  2. *Zhu, Y., Chowdhury, F., Fu, H., Moody, A., Mohror, K., Sato, K., and Yu, W.:
    “Entropy-Aware I/O Pipelining for Large-Scale Deep Learning on HPC Systems”
    IEEE International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2018), Milwaukee, USA. (2018).
  3. *Wang, T., Moody, A., Zhu, Y., Mohror, K., Sato, K., Islam, T., and Yu, W.:
    "MetaKV: A Key-Value Store for Metadata Management of Distributed Burst Buffers"
    In Proceedings of the International Conference on Parallel and Distributed Processing Symposium 2017 (IPDPS2017), Orlando, USA. (2017).
  4. *Sato, K., Ahn, D. H., Laguna, I., Lee, G. L., Schulz, M., and Chambreau, C. M.:
    "Noise Injection Techniques for Reproducing Subtle and Unintended Message Races"
    Proceedings of the 20th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP17), Austin, USA. (2017).
  5. *Xu, T., Sato, K., and Matsuoka, S.:
    "CloudBB: Scalable I/O Accelerator for Shared Cloud Storage"
    The 22nd IEEE International Conference on Parallel and Distributed Systems (ICPADS 2016), Wuhan, China. (2016)
  6. *Wang, T., Mohror, K., Moody, A., Sato, K., and Yu, W.:
    "An Ephemeral Burst-Buffer File System for Scientific Applications"
    In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis 2016 (SC16), Salt Lake City, USA. (2016).
  7. *Sato, K., Ahn, D. H., Laguna, I., Lee, G. L., Schulz, M.:
    "Clock Delta Compression for Scalable Order-Replay of Non-Deterministic Parallel Applications"
    In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis 2015 (SC15), Austin, USA. (2015).
  8. *Sasaki, N., Sato, K., Endo, T., Matsuoka, S.:
    "Exploration of Lossy Compression for Application-level Checkpoint/Restart"
    In Proceedings of the International Conference on Parallel and Distributed Processing Symposium 2015 (IPDPS2015), Hyderabad, INDIA. (2015).
  9. Sato, K., Mohror, K., Moody, A., Gamblin, T., de Supinski, B. R., Maruyama, N., and Matsuoka, S.:
    "A User-level InfiniBand-based File System and Checkpoint Strategy for Burst Buffers"
    In Proceedings of the 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid2014), Chicago, USA. (2014).
  10. Sato, K., Moody, A., Mohror, K., Gamblin, R., de Supinski, B. R., Maruyama, N., and Matsuoka, S.:
    "Design and Modeling of a Non-blocking Checkpointing System"
    In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis 2012 (SC12), Salt Lake, USA. (2012).

Contact information

RIKEN Center for Computational Science(R-CCS)
7-1-26 Minatojima-minami-machi,
Chuo-ku, Kobe, Hyogo
650-0047, Japan
Tel: +81-(0)78-940-5840
Fax: +81-(0)78-304-4963

Email: kento.sato [at] riken.jp

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