Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units.
Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units.
复制标题
图形处理单元加速的3D浊度介质中光子迁移的蒙特卡洛模拟。
DOI：
10.1364/oe.17.020178
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发表时间：
2009-10-26
期刊：
影响因子：
3.8
通讯作者：
Boas, David A.
中科院分区：
文献类型：
--
作者：
Fang, Qianqian;Boas, David A.
本文提出了一种基于图形处理器（GPU）加速的并行蒙特卡罗算法，用于模拟任意三维混浊介质中的时间分辨光子迁移。通过利用大规模并行线程和低内存延迟，该算法允许在GPU中同时模拟多个光子。为了进一步提高计算效率，我们探索了两个并行的随机数发生器（RNG），其中包括一个基于混沌格的纯浮点RNG。沿着了一种有效的边界反射方法和时间分辨成像函数。对于一个均匀的半无限介质，观察到良好的协议之间的模拟输出和解析解从扩散理论。代码用CUDA编程语言实现，并在不同的参数下进行了基准测试，如线程数，RNG的选择和内存访问模式。在低成本的图形卡上，当使用1792个并行线程时，该算法的加速比超过传统CPU计算的300。当使用原子操作时，加速比下降到75。这些结果使基于GPU的蒙特卡罗模拟成为一种实用的解决方案，用于在广泛的漫射光学成像应用中进行数据分析，例如人脑或小动物成像。
We report a parallel Monte Carlo algorithm accelerated by graphics processing units (GPU) for modeling time-resolved photon migration in arbitrary 3D turbid media. By taking advantage of the massively parallel threads and low-memory latency, this algorithm allows many photons to be simulated simultaneously in a GPU. To further improve the computational efficiency, we explored two parallel random number generators (RNG), including a floating-point-only RNG based on a chaotic lattice. An efficient scheme for boundary reflection was implemented, along with the functions for time-resolved imaging. For a homogeneous semi-infinite medium, good agreement was observed between the simulation output and the analytical solution from the diffusion theory. The code was implemented with CUDA programming language, and benchmarked under various parameters, such as thread number, selection of RNG and memory access pattern. With a low-cost graphics card, this algorithm has demonstrated an acceleration ratio above 300 when using 1792 parallel threads over conventional CPU computation. The acceleration ratio drops to 75 when using atomic operations. These results render the GPU-based Monte Carlo simulation a practical solution for data analysis in a wide range of diffuse optical imaging applications, such as human brain or small-animal imaging.