Performance Optimisations for a Numerical Solution to a 3D Model of Tumour-Induced Angiogenesis on a Parallel Programming Platform
Cell Biology
Volume 3, Issue 3, September 2015, Pages: 38-49
Received: Sep. 9, 2015; Accepted: Sep. 23, 2015; Published: Oct. 28, 2015
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Paul M. Darbyshire, Department of Computational Biophysics, Algenet Cancer Research, Nottingham, UK
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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.
Tumour-Induced Angiogenesis, Compute Unified Device Architecture (CUDA), Graphical Processing Unit (GPU), High-Performance Computing (HPC)
To cite this article
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. doi: 10.11648/j.cb.20150303.11
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.
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