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Graphics processing unit-accelerated mesh-based Monte Carlo photon transport simulations

机译:图形处理单元基于网格的加速蒙特卡洛光子传输模拟

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The mesh-based Monte Carlo (MMC) algorithm is increasingly used as the gold-standard for developing new biophotonics modeling techniques in 3-D complex tissues, including both diffusion-based and various Monte Carlo (MC)-based methods. Compared to multilayered and voxel-based MCs, MMC can utilize tetrahedral meshes to gain improved anatomical accuracy but also results in higher computational and memory demands. Previous attempts of accelerating MMC using graphics processing units (GPUs) have yielded limited performance improvement and are not publicly available. We report a highly efficient MMC—MMCL—using the OpenCL heterogeneous computing framework and demonstrate a speedup ratio up to 420x compared to state-of-the-art single-threaded CPU simulations. The MMCL simulator supports almost all advanced features found in our widely disseminated MMC software, such as support for a dozen of complex source forms, wide-field detectors, boundary reflection, photon replay, and storing a rich set of detected photon information. Furthermore, this tool supports a wide range of GPUs/CPUs across vendors and is freely available with full source codes and benchmark suites at http://mcx.space/#mmc.
机译:基于网格的蒙特卡洛(MMC)算法越来越多地被用作开发3-D复杂组织中新的生物光子建模技术的金标准,包括基于扩散的方法和基于蒙特卡洛(MC)的各种方法。与多层和基于体素的MC相比,MMC可以利用四面体网格获得更高的解剖精度,但也导致更高的计算和内存需求。先前使用图形处理单元(GPU)加速MMC的尝试只能带来有限的性能改进,并且尚未公开。我们报告了一种使用OpenCL异构计算框架的高效MMC — MMCL,与最先进的单线程CPU仿真相比,其加速比高达420倍。 MMCL仿真器支持在我们广泛传播的MMC软件中发现的几乎所有高级功能,例如,支持多种复杂的源形式,宽视场检测器,边界反射,光子重播以及存储大量检测到的光子信息。此外,该工具支持各供应商提供的广泛的GPU / CPU,可从http://mcx.space/#mmc免费获得完整的源代码和基准套件。

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