Performance Optimisations for a Numerical Solution to a 3D Model of Tumour-Induced Angiogenesis on a Parallel Programming Platform
Volume 3, Issue 3, September 2015, Pages: 38-49
Received: Sep. 9, 2015;
Accepted: Sep. 23, 2015;
Published: Oct. 28, 2015
Views 2718 Downloads 82
Paul M. Darbyshire, Department of Computational Biophysics, Algenet Cancer Research, Nottingham, UK
The challenging issues of cancer prevention and cure lie in the need for a more detailed knowledge of the dynamic processes and mechanisms of cellular behaviour and tumour growth dynamics. In this paper we extend a previous 2D parallel implementation of a continuous-discrete model of tumour-induced angiogenesis to the more realistic 3D case. In particular, we look in-depth at available performance optimisation techniques to further improve the computational method and explore in more detail the hardware architecture. Recent evidence clearly indicates that GPU-accelerated computing can greatly facilitate researchers, clinicians and oncologists by performing time-saving in-silico experiments that have the potential to assist in quantifying cellular parameters, highlight model features, and help explore new cancer treatments and therapies.
Paul M. Darbyshire,
Performance Optimisations for a Numerical Solution to a 3D Model of Tumour-Induced Angiogenesis on a Parallel Programming Platform, Cell Biology.
Vol. 3, No. 3,
2015, pp. 38-49.
Darbyshire, P. M. Coupled Nonlinear Partial Differential Equations Describing Avascular Tumour Growth Are Solved Numerically Using Parallel Programming to Assess Computational Speedup. Computational Biology and Bioinformatics. Vol. 3, No. 5, 65-73. 2015.
Darbyshire, P. M. The Numerical Solution of a Hybrid Continuous-Discrete Model of Tumour-Induced Angiogenesis is Implemented in Parallel and Performance Improvements Analysed. European Journal of Biophysics. Vol. 7, No. 4, 167-182. 2015.
Albini, A., Tosetti, A. F., Li, W. V., Noonan, D. M. and Li, W. W. Cancer prevention by targeting angiogenesis Nature Reviews Clinical Oncology 9, 498-509. 2012.
Ferrara, N. and Kerbel, R. S. Angiogenesis as a therapeutic target. Nature, 438 967–974. 2005.
Carmeliet, P. Angiogenesis in life, disease and medicine. Nature, 438: 932–936. 2005.
Bouard S. de, Herlin, P. and Christensen, J. G. Antiangio-genic and anti-invasive effects of sunitinib on experimental human glioblastoma. Neuro-Oncology, Vol. 9, No. 4, 412– 423. 2007.
Norden, A. D, Drappatz, J. and Wen P. Y. Novel antiangiogenic therapies for malignant gliomas. The Lancet Neurology, Vol. 7, No. 12, 1152–1160. 2008.
Peirce, S. M. Computational and mathematical modeling of angiogenesis. Microcirculation, 15(8), 739–751. 2008.
M. Scianna, M., Bell. C. and Preziosi L. A review of mathematical models for the formation of vascular networks. Oxford Centre for Collaborative Applied Mathematics. 2012.
Anderson, A.R.A. and Chaplain, M. Continuous and discrete mathematical models of tumour-induced angiogenesis, Bulletin of Mathematical Biology, 60, 857-900. 1998.
Anderson, A., B. D. S. Sleeman, I. M. Young and B. S. Griffiths. Nematode movement along a chemical gradient in a structurally heterogeneous environment: II. Theory. Fundamental and Applied Nematology, 20, 165–172. 1997.
NVIDIA’s Next Generation CUDA Compute Architecture: Kepler GK110. Whitepaper. NVIDIA Corporation. 2012.
Nvidia Corporation. CUDA C programming guide. Version 6.0. 2014.
CUDA C BEST PRACTICES GUIDE. NVIDIA Corporation. 2015.
Cheng, J., Grossman, M and McKercher, Ty. Professional CUDA C Programming. Wrox. 2014.
Venkatasubramanian, S. and Vuduc, R. W. Tuned and wildly asynchronous stencil kernels for hybrid CPU/GPU systems. In Proceedings of the Association of Computing Machinery International Conference on Supercomputing, New York. 2009.
Amorim, R., Haase, G., Liebmann, M. and Weber dos Santos, R. Comparing CUDA and OpenGL implementations for a Jacobi iteration. In Proceedings of High Performance Computing and Simulation Conference, Berlin. 2009.
Cecilia, J. M., Garcıa, J. M. and Ujaldon, M. CUDA 3D stencil computations for the Jacobi method. Springer, 173-183. 2012.