Chapter

Methods and Tools of Parallel Programming Multicomputers

Volume 6083 of the series Lecture Notes in Computer Science pp 222-231

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

  • Norma AliasAffiliated withLancaster UniversityIbnu Sina Institute, Faculty of Science, University Technology
  • , Md. Rajibul IslamAffiliated withLancaster UniversityFaculty of Information Science and Technology, Multimedia University
  • , Rosdiana ShahirAffiliated withCarnegie Mellon UniversityDepartment of Mathematics, Faculty of Science, University Technology
  • , Hafizah HamzahAffiliated withCarnegie Mellon UniversityDepartment of Mathematics, Faculty of Science, University Technology
  • , Noriza SatamAffiliated withCarnegie Mellon UniversityDepartment of Mathematics, Faculty of Science, University Technology
  • , Zarith SafizaAffiliated withCarnegie Mellon UniversityDepartment of Mathematics, Faculty of Science, University Technology
  • , Roziha DarwisAffiliated withCarnegie Mellon UniversityDepartment of Mathematics, Faculty of Science, University Technology
  • , Eliana LudinAffiliated withLancaster UniversityIbnu Sina Institute, Faculty of Science, University Technology
  • , Masrin AzamiAffiliated withLancaster UniversityIbnu Sina Institute, Faculty of Science, University Technology

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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