10837

Accelerated rescaling of single Monte Carlo simulation runs with the Graphics Processing Unit (GPU)

Owen Yang, Bernard Choi
Department of Biomedical Engineering, University of California, Irvine, 3120 Natural Sciences II, Irvine, CA
Biomedical Optics Express, Volume 4, Issue 11, Page 2667, 2013

@article{yang2013accelerated,

   title={Accelerated rescaling of single Monte Carlo simulation runs with the Graphics Processing Unit (GPU)},

   author={Yang, Owen and Choi, Bernard},

   journal={Biomedical Optics Express},

   volume={4},

   number={11},

   pages={2667–2672},

   year={2013},

   publisher={Optical Society of America}

}

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To interpret fiber-based and camera-based measurements of remitted light from biological tissues, researchers typically use analytical models, such as the diffusion approximation to light transport theory, or stochastic models, such as Monte Carlo modeling. To achieve rapid (ideally real-time) measurement of tissue optical properties, especially in clinical situations, there is a critical need to accelerate Monte Carlo simulation runs. In this manuscript, we report on our approach using the Graphics Processing Unit (GPU) to accelerate rescaling of single Monte Carlo runs to calculate rapidly diffuse reflectance values for different sets of tissue optical properties. We selected MATLAB to enable non-specialists in C and CUDA-based programming to use the generated open-source code. We developed a software package with four abstraction layers. To calculate a set of diffuse reflectance values from a simulated tissue with homogeneous optical properties, our rescaling GPU-based approach achieves a reduction in computation time of several orders of magnitude as compared to other GPU-based approaches. Specifically, our GPU-based approach generated a diffuse reflectance value in 0.08ms. The transfer time from CPU to GPU memory currently is a limiting factor with GPU-based calculations. However, for calculation of multiple diffuse reflectance values, our GPU-based approach still can lead to processing that is ~3400 times faster than other GPU-based approaches.
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