Next-generation acceleration and code optimization for light transport in turbid media using GPUs

Erik Alerstam, William C. Yip Lo, Tianyi D. Han, Jonathan Rose, Stefan Andersson-Engels, Lothar Lilge
Department of Physics, Lund University, Sweden
Biomedical Optics Express, Vol. 1, No. 2. (1 September 2010), pp. 658-675


   title={Next-generation acceleration and code optimization for light transport in turbid media using GPUs},

   author={Alerstam, E. and Yip Lo, W.C. and Han, T.D. and Rose, J. and Andersson-Engels, S. and Lilge, L.},

   journal={Biomedical Optics Express},





   publisher={Optical Society of America}


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A highly optimized Monte Carlo (MC) code package for simulating light transport is developed on the latest graphics processing unit (GPU) built for general-purpose computing from NVIDIA – the Fermi GPU. In biomedical optics, the MC method is the gold standard approach for simulating light transport in biological tissue, both due to its accuracy and its flexibility in modelling realistic, heterogeneous tissue geometry in 3-D. However, the widespread use of MC simulations in inverse problems, such as treatment planning for PDT, is limited by their long computation time. Despite its parallel nature, optimizing MC code on the GPU has been shown to be a challenge, particularly when the sharing of simulation result matrices among many parallel threads demands the frequent use of atomic instructions to access the slow GPU global memory. This paper proposes an optimization scheme that utilizes the fast shared memory to resolve the performance bottleneck caused by atomic access, and discusses numerous other optimization techniques needed to harness the full potential of the GPU. Using these techniques, a widely accepted MC code package in biophotonics, called MCML, was successfully accelerated on a Fermi GPU by approximately 600x compared to a state-of-the-art Intel Core i7 CPU. A skin model consisting of 7 layers was used as the standard simulation geometry. To demonstrate the possibility of GPU cluster computing, the same GPU code was executed on four GPUs, showing a linear improvement in performance with an increasing number of GPUs. The GPU-based MCML code package, named GPU-MCML, is compatible with a wide range of graphics cards and is released as an open-source software in two versions: an optimized version tuned for high performance and a simplified version for beginners (http://code.google.com/p/gpumcml).
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