Schweiger Martin
Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK.
Int J Biomed Imaging. 2011;2011:403892. doi: 10.1155/2011/403892. Epub 2011 Oct 16.
We introduce a GPU-accelerated finite element forward solver for the computation of light transport in scattering media. The forward model is the computationally most expensive component of iterative methods for image reconstruction in diffuse optical tomography, and performance optimisation of the forward solver is therefore crucial for improving the efficiency of the solution of the inverse problem. The GPU forward solver uses a CUDA implementation that evaluates on the graphics hardware the sparse linear system arising in the finite element formulation of the diffusion equation. We present solutions for both time-domain and frequency-domain problems. A comparison with a CPU-based implementation shows significant performance gains of the graphics accelerated solution, with improvements of approximately a factor of 10 for double-precision computations, and factors beyond 20 for single-precision computations. The gains are also shown to be dependent on the mesh complexity, where the largest gains are achieved for high mesh resolutions.
我们介绍了一种用于计算散射介质中光传输的GPU加速有限元正向求解器。正向模型是漫射光学层析成像中迭代图像重建方法计算量最大的部分，因此正向求解器的性能优化对于提高逆问题的求解效率至关重要。GPU正向求解器采用CUDA实现，在图形硬件上对扩散方程有限元公式中产生的稀疏线性系统进行评估。我们给出了时域和频域问题的解决方案。与基于CPU的实现进行比较表明，图形加速解决方案具有显著的性能提升，双精度计算提高了约10倍，单精度计算提高了20倍以上。结果还表明，性能提升还取决于网格复杂度，在高网格分辨率下可实现最大的性能提升。
