Parallel System for Abnormal Cell Growth Prediction Based on Fast Numerical Simulation

  • Norma Alias
  • Md. Rajibul Islam
  • Rosdiana Shahir
  • Hafizah Hamzah
  • Noriza Satam
  • Zarith Safiza
  • Roziha Darwis
  • Eliana Ludin
  • Masrin Azami
Conference paper

DOI: 10.1007/978-3-642-14822-4_25

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6083)
Cite this paper as:
Alias N. et al. (2010) Parallel System for Abnormal Cell Growth Prediction Based on Fast Numerical Simulation. In: Hsu CH., Malyshkin V. (eds) Methods and Tools of Parallel Programming Multicomputers. MTPP 2010. Lecture Notes in Computer Science, vol 6083. Springer, Berlin, Heidelberg

Abstract

The paper focuses on a numerical method for detecting, visualizing and monitoring abnormal cell growth using large-scale mathematical simulations. The discretization of multi-dimensional partial differential equation (PDE) is based on finite difference method. The predictor system depending on users input data via a user interface, generating the initial and boundary condition generated from parabolic or elliptic type of PDE. The processing large sparse matrixes are based on multiprocessor computer systems for abnormal growth visualization. The multi-dimensional abnormal cell has produced the numerical analysis and understanding results at the target area for the potential improvement of detection and monitoring the growth. The development of the prediction system is the combinations of the parallel algorithms, open source software on Linux environment and distributed multiprocessor system. The paper ends with a concluding remark on the parallel performance evaluations and numerical analysis in reducing the execution time, communication cost and computational complexity.

Keywords

parallel system abnormal cell growth simulation IADE method AGE method distributed memory systems 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Norma Alias
    • 1
  • Md. Rajibul Islam
    • 2
  • Rosdiana Shahir
    • 3
  • Hafizah Hamzah
    • 3
  • Noriza Satam
    • 3
  • Zarith Safiza
    • 3
  • Roziha Darwis
    • 3
  • Eliana Ludin
    • 1
  • Masrin Azami
    • 1
  1. 1.Ibnu Sina Institute, Faculty of ScienceUniversity TechnologyMalaysia
  2. 2.Faculty of Information Science and TechnologyMultimedia UniversityMalaysia
  3. 3.Department of Mathematics, Faculty of ScienceUniversity TechnologyMalaysia

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