Open AccessHigh‐speed classification of coherent X‐ray diffraction patterns on the K computer for high‐resolution single biomolecule imaging
Author(s) -
Tokuhisa Atsushi,
Arai Junya,
Joti Yasumasa,
Ohno Yoshiyuki,
Kameyama Toyohisa,
Yamamoto Keiji,
Hatanaka Masayuki,
Gerofi Balazs,
Shimada Akio,
Kurokawa Motoyoshi,
Shoji Fumiyoshi,
Okada Kensuke,
Sugimoto Takashi,
Yamaga Mitsuhiro,
Tanaka Ryotaro,
Yokokawa Mitsuo,
Hori Atsushi,
Ishikawa Yutaka,
Hatsui Takaki,
Go Nobuhiro
Publication year - 2013
Publication title -
journal of synchrotron radiation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.172
H-Index - 99
ISSN - 1600-5775
DOI - 10.1107/s0909049513022152
Subject(s) - diffraction , optics , laser , computer science , noise (video) , resolution (logic) , electron diffraction , supercomputer , physics , artificial intelligence , image (mathematics) , operating system
Single‐particle coherent X‐ray diffraction imaging using an X‐ray free‐electron laser has the potential to reveal the three‐dimensional structure of a biological supra‐molecule at sub‐nanometer resolution. In order to realise this method, it is necessary to analyze as many as 1 × 10 6 noisy X‐ray diffraction patterns, each for an unknown random target orientation. To cope with the severe quantum noise, patterns need to be classified according to their similarities and average similar patterns to improve the signal‐to‐noise ratio. A high‐speed scalable scheme has been developed to carry out classification on the K computer, a 10PFLOPS supercomputer at RIKEN Advanced Institute for Computational Science. It is designed to work on the real‐time basis with the experimental diffraction pattern collection at the X‐ray free‐electron laser facility SACLA so that the result of classification can be feedback for optimizing experimental parameters during the experiment. The present status of our effort developing the system and also a result of application to a set of simulated diffraction patterns is reported. About 1 × 10 6 diffraction patterns were successfully classificatied by running 255 separate 1 h jobs in 385‐node mode.