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Onishi, Ryoichi*; Guo, Z.*; Kimura, Toshiya*; Iwamiya, Toshiyuki*
Proceedings of 4th International Conference on Supercomputing in Nuclear Applications (SNA 2000) (CD-ROM), 12 Pages, 2000/09
no abstracts in English
Hasegawa, Yukihiro*; Yamagishi, Nobuhiro*; Takemiya, Hiroshi*; Hirayama, Toshio; Shirai, Hiroshi; Shimizu, Katsuhiro; Ozeki, Takahisa
Keisan Kogaku Koenkai Rombunshu, p.365 - 368, 2000/05
no abstracts in English
Takemiya, Hiroshi*; Yamagishi, Nobuhiro*; Imamura, Toshiyuki; Ueno, Koichi*; Koide, Hiroshi; Tsujita, Yuichi; Hasegawa, Yukihiro*; Higuchi, Kenji; Matsuda, Katsuyuki*; Hirayama, Toshio
JAERI-Data/Code 2000-013, p.52 - 0, 2000/03
JAERI-Data-Code-2000-013.pdf:8.64MB
no abstracts in English
Takemiya, Hiroshi*; Yamagishi, Nobuhiro*; Imamura, Toshiyuki; Ueno, Koichi*; Koide, Hiroshi; Tsujita, Yuichi; Hasegawa, Yukihiro*; Higuchi, Kenji; Matsuda, Katsuyuki*; Hirayama, Toshio
JAERI-Data/Code 2000-010, p.49 - 0, 2000/02
JAERI-Data-Code-2000-010.pdf:2.04MB
no abstracts in English
Onishi, Ryoichi*; ; Guo, Z.*;
CEAS/AIAA/ICASE/NASA Langley Int. Forum on Aeroelasticity and Strucrual Dynamics 1999, (2), p.483 - 489, 1999/06
no abstracts in English
Onishi, Ryoichi*; ; Guo, Z.*;
A Collection of the 17th AIAA Applied Aerodynamics Conf. Technical Papers, p.492 - 496, 1999/00
no abstracts in English
Yokokawa, Mitsuo; Watanabe, Kenji*; ; ;
Computational Fluid Dynamics Journal, 1(3), p.337 - 346, 1992/10
no abstracts in English
Watanabe, Kenji*; ; Yokokawa, Mitsuo; ;
Joho Shori Gakkai Kenkyu Hokoku, 91(61), p.17 - 24, 1991/07
no abstracts in English
Guo, Z.; Nishida, Akemi; Choi, B.; Nakajima, Norihiro
no journal, ,
In the field of seismic analysis of nuclear facilities, large-scale parallel analyses using numerical models with several hundred millions of DOFs are becoming possible by the recent advances in high-performance parallel computing technologies. In dealing with such three dimensional time series data, the post-processing may be often more difficult than the seismic response simulation itself. The purpose of the current study is to develop a parallel visualization application, which can visualize large-scale simulation results (distributed time series data) effectively. In this report, we show an approach to increase the efficiency of parallel visualization by more than 200 times by using appropriate pre-processing for this kind of large-scale distributed time series data.