Kyushu University Academic Staff Educational and Research Activities Database
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Kenji Ono Last modified date:2019.05.06

Professor / Graduate School and Faculty of Information Science and Electrical Engineering, Department of Informatics
Interdisciplinary Computational Science Section
Research Institute for Information Technology

Graduate School
Undergraduate School
Other Organization
Administration Post
Director of the Research Institute for Information Technology

Academic Degree
Dr. Eng.
Country of degree conferring institution (Overseas)
Field of Specialization
Computational Fluid Dynamics, Visualization, High-performance parallel computing
ORCID(Open Researcher and Contributor ID)
Total Priod of education and research career in the foreign country
Outline Activities
Research – Computational fluid dynamics simulation, Visualization, Parallel computing;

Education – Numerical analysis and its exercise;

Service – Operation and support of supercomputers;

Others – Commissioned project of science and technology research funded by MEXT, CREST, JHPCN;
Research Interests
  • Research and development for in-situ / in-transit visualization / data processing infrastructure
    keyword : visualization system, parallel processing, usability, remote processing
  • Finding natural lows from data using deep learning
    keyword : Deep learning, Genetic programming, Lasso
  • Interaction between real world and virtual world through VR/AR technology
    keyword : HMD, UI
  • Reconstruction of turbulent model using machine learning
    keyword : Deep learning, LES turbulence modeling, CFD
  • Research of parallel computing method in time integration
    keyword : multi-grid algorithm in time, parareal method
  • Research and development of technologies for upstream design
    keyword : Derivation of idea
  • Research of large-scale parallel grid generation
    keyword : CAD data, geometry, CFD
  • Construction of an environment to support execution of simulation
    keyword : workflow, data management, multi-platform, eco-system
  • Technology development of large-scale parallel visualization system
    keyword : sort-last image compositing, ray tracing, multi-platform, data model
  • Development of thermal flow simulator for flow around complex geometries
    keyword : Cartesian mesh, Mesh generation
Current and Past Project
  • Develop an integrated framework to manage various data to design products, analytic tools to extract data by various methods, and useful display technology to visualize information. Then, apply the developed system to validation cases, and confirm effectiveness.
Academic Activities
1. Fan Hong, Chongke Bi, Hanqi Guo, Kenji Ono, Xiaoru Yuan, Compression-based integral curve data reuse framework for flow visualization, Journal of Visualization, 10.1007/s12650-017-0428-4, 20, 4, 859-874, 2017.11, [URL], Currently, by default, integral curves are repeatedly re-computed in different flow visualization applications, such as FTLE field computation, source-destination queries, etc., leading to unnecessary resource cost. We present a compression-based data reuse framework for integral curves, to greatly reduce their retrieval cost, especially in a resource-limited environment. In our design, a hierarchical and hybrid compression scheme is proposed to balance three objectives, including high compression ratio, controllable error, and low decompression cost. Specifically, we use and combine digitized curve sparse representation, floating-point data compression, and octree space partitioning to adaptively achieve the objectives. Results have shown that our data reuse framework could acquire tens of times acceleration in the resource-limited environment compared to on-the-fly particle tracing, and keep controllable information loss. Moreover, our method could provide fast integral curve retrieval for more complex data, such as unstructured mesh data..
2. Seigo Imamura, Kenji Ono, Mitsuo Yokokawa, Iterative-method performance evaluation for multiple vectors associated with a large-scale sparse matrix, International Journal of Computational Fluid Dynamics, 10.1080/10618562.2016.1234046, 30, 6, 395-401, 2016.07, Ensemble computing, which is an instance of capacity computing, is an effective computing scenario for exascale parallel supercomputers. In ensemble computing, there are multiple linear systems associated with a common coefficient matrix. We improve the performance of iterative solvers for multiple vectors by solving them at the same time, that is, by solving for the product of the matrices. We implemented several iterative methods and compared their performance. The maximum performance on Sparc VIIIfx was 7.6 times higher than that of a naïve implementation. Finally, to deal with the different convergence processes of linear systems, we introduced a control method to eliminate the calculation of already converged vectors..
1. Seigo Imamura, Mikio Iizuka, Kenji Ono, Mitsuo Yokokawa, Building the Performance Model of Parareal Method, 28th International Conference on Parallel Computational Fluid Dynamics Parallel CFD2016, 2016.05.
2. Mikio Iizuka, Kenji Ono, Convergence Rate of Parareal Method with Modified Newmark-Beta Algorithm for 2nd-Order ODE, 17th SIAM Conference on Parallel Processing for Scientific Computing, 2016.04.
Membership in Academic Society
  • Information Processing Society of Japan
  • The Japan Society for Computational Engineering and Science
  • The Visualization Society of Japan
  • The Japan Society of Mechanical Engineers
  • Society of Automotive Engineers of Japan
  • The Japan Society of Fluid Mechanics
  • IEEE