Parallel System for Abnormal Cell Growth Prediction Based on Fast Numerical Simulation
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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.
Keywordsparallel system abnormal cell growth simulation IADE method AGE method distributed memory systems
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- 3.Chato, J.C.: Fundamentals of bioheat transfer. In: Gautherie, M. (ed.) Thermal dosimetry and treatment planning, pp. 1–56. Springer, Berlin (1990)Google Scholar
- 4.Norma, A., bin Masseri, M.I.S., Rajibul Islam, M., Khalid, S.N.: The Visualization of Three Dimensional Brain Tumors’ Growth on Distributed Parallel Computer Systems. Journal of Applied Sciences. Asian Network for Scientific Information (ANSINET) 9(3), 505–512 (2009)Google Scholar
- 5.Norma, A., Norfarizan, M.S., Pheng, H.S.: High performance simulation for brain tumors growth using parabolic equation on heterogeneous parallel computer systems. Journal of Information Technology and Multimedia 4, 39–52 (2007)Google Scholar
- 6.Sahimi, M.S., Mansor, N.A., Nor, N.M., Nusi, N.M., Norma, A.: A High accuracy variant of the Iterative Alternating Decomposition Explicit Method for Solving the Heat Equation. International Journal of Simulation and processing Modelling 2(½), 45–49 (2006)Google Scholar
- 7.Norma, A., Norfarizan, Hidayah, S.N.K., Dolly, S., Phang, T.I.: The parallelism algorithm of human tumor growth using high performance computing. In: The 2nd International Conference on Pervasive Computing Technologies for Healthcare, Tampere, Finland (2008)Google Scholar
- 8.Norma, A., Norfarizan, M.S., Hidayah, S.N.K.: High Performance Visualization of Human Tumor Growth Software. In: Palma, J.M.L.M., Amestoy, P.R., Daydé, M., Mattoso, M., Lopes, J.C. (eds.) VECPAR 2008. LNCS, vol. 5336, pp. 591–599. Springer, Heidelberg (2008)Google Scholar
- 9.Evans, D.J., Sahimi, M.S.: The Alternating Group Explicit Iterative method for Solving Parabolic Equations. Intern. J. Computer math. 24, 127–145 (1988)Google Scholar