Waudby, CA;
Christodoulou, J;
(2011)
GPU accelerated Monte Carlo simulation of pulsed-field gradient NMR experiments.
Journal of Magnetic Resonance
, 211
(1)
67 - 73.
10.1016/j.jmr.2011.04.004.
Text
Waudby_2011 J. Magn. Reson.pdf Access restricted to UCL open access staff Download (554kB) |
Abstract
The simulation of diffusion by Monte Carlo methods is often essential to describing NMR measurements of diffusion in porous media. However, simulation timescales must often span hundreds of milliseconds, with large numbers of trajectories required to ensure statistical convergence. Here we demonstrate that by parallelising code to run on graphics processing units (GPUs), these calculations may be accelerated by over three orders of magnitude, opening new frontiers in experimental design and analysis. As such cards are commonly installed on most desktop computers, we expect that this will prove useful in many cases where simple analytical descriptions are not available or appropriate, e.g. in complex geometries or where short gradient pulse approximations do not hold, or for the analysis of diffusion-weighted MRI in complex tissues such as the lungs and brain. (C) 2011 Elsevier Inc. All rights reserved.
Type: | Article |
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Title: | GPU accelerated Monte Carlo simulation of pulsed-field gradient NMR experiments |
DOI: | 10.1016/j.jmr.2011.04.004 |
Publisher version: | http://dx.doi.org/10.1016/j.jmr.2011.04.004 |
Language: | English |
Keywords: | CUDA, GPGPU, Parallel processing, Restricted diffusion, PGSE, Restricted diffusion, Self-diffusion, Spin-echo, Laplacian Eigenfunctions, Length scales, Approximation, Spectroscopy, Time, Diffusometry, Attenuation |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Structural and Molecular Biology |
URI: | https://discovery.ucl.ac.uk/id/eprint/1307844 |
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