Accelerating mesh-based Monte Carlo method on modern CPU architectures

Fang, Qianqian; Kaeli, David

doi:10.1364/boe.3.003223

Cited by 76 publications

(54 citation statements)

References 17 publications

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“…While TIM-OS used compiler autovectorization and the Intel Math Kernel Library (MKL) to achieve some of the gains, vectorization of key distributions such as HenyeyGreenstein is both crucial to MC simulation performance and beyond the ability of compilers to do automatically. This extends the approach similar taken by Fang and Kaeli 16 in enhancing MMC, most notably using AVX2 instructions where available, hand-writing the key ray-tetrahedron intersection code, and vectorizing the Henyey-Greenstein distribution. Further advantage is gained by using Cartesian coordinates over barycentric coordinates, which is unlikely to lead to precision issues in a well-conditioned mesh.…”

Section: Performancementioning

confidence: 66%

High-performance, robustly verified Monte Carlo simulation with FullMonte

et al. 2018

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Abstract. We introduce the FullMonte tetrahedral 3-D Monte Carlo (MC) software package for simulation, visualization, and analysis of light propagation in heterogeneous turbid media including tissue. It provides the highest computational performance and richest set of input, output, and analysis facilities of any open-source tetrahedral-mesh MC light simulator. It also provides a robust framework for statistical verification. A scripting interface makes set-up of simulation runs simple, including parameter sweeps, while simultaneously providing customization options. Data formats shared with class-leading visualization tools, VTK and Paraview, facilitate interactive generation of publication-quality fluence and irradiance maps. The simulator can read and write file formats supported by other similar simulators, such as TIM-OS, MMC, COMSOL (finite-element simulations), and MCML to support comparison. Where simulator features permit, FullMonte can take a single test case, run it in multiple software packages, and load the results together for comparison. Example meshes, optical properties, set-up scripts, and output files are provided for user convenience. We demonstrate its use in several test cases, including photodynamic therapy of the brain, bioluminescence imaging (BLI) in a mouse phantom, and a comparison against MCML for layered geometries. Application domains that can benefit from use of FullMonte include photodynamic, photothermal, and photobiomodulation therapies, BLI, diffuse optical tomography, MC software development, and biophotonics education. Since MC results may be used for preclinical or even clinical experiments, a robust and rigorous verification process is essential. Being a stochastic numerical method, MC simulation has unique challenges associated with verification of output results since observed differences may be due simply to output variance or actual differences in expected output. We describe and have implemented a rigorous and statistically justified framework for comparing between simulators of the same class and for performing regression testing. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Performancementioning

confidence: 66%

High-performance, robustly verified Monte Carlo simulation with FullMonte

et al. 2018

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“…3(a)] provides spatial priors which can be used to separate the effects of fluorophore depth and concentration in the diffuse-light reconstruction. 44,48 Light transport models that are more accurate near the tissue surface, such as Monte Carlo modeling, 49,50 or higher-order solutions to radiation transport 51 could further improve the ability to quantify fluorescence concentration, especially relatively near the tissue surface. 3(b)].…”

Section: Discussionmentioning

confidence: 99%

Macroscopic-imaging technique for subsurface quantification of near-infrared markers during surgery

et al. 2015

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Obtaining accurate quantitative information on the concentration and distribution of fluorescent markers lying at a depth below the surface of optically turbid media, such as tissue, is a significant challenge. Here, we introduce a fluorescence reconstruction technique based on a diffusion light transport model that can be used during surgery, including guiding resection of brain tumors, for depth-resolved quantitative imaging of near-infrared fluorescent markers. Hyperspectral fluorescence images are used to compute a topographic map of the fluorophore distribution, which yields structural and optical constraints for a three-dimensional subsequent hyperspectral diffuse fluorescence reconstruction algorithm. Using the model fluorophore Alexa Fluor 647 and brain-like tissue phantoms, the technique yielded estimates of fluorophore concentration within ±25% of the true value to depths of 5 to 9 mm, depending on the concentration. The approach is practical for integration into a neurosurgical fluorescence microscope and has potential to further extend fluorescence-guided resection using objective and quantified metrics of the presence of residual tumor tissue.

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“…As we discussed previously, 7 at the core of MC light transport modeling is a ray-tracing algorithm that propagates photons through complex media. In our publicly available MMC software, 6 we have implemented 4 different raytracers to advance photons from one tetrahedron to the next.…”

Section: Gpu-accelerated Photon Propagation In Tetrahedral Meshesmentioning

confidence: 99%

GPU-accelerated mesh-based Monte Carlo photon transport simulations

Fang

Yan

2019

Preprint

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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 multi-layered 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. Here 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 singlethreaded 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. c 2019.

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Accelerating mesh-based Monte Carlo method on modern CPU architectures

Cited by 76 publications

References 17 publications

High-performance, robustly verified Monte Carlo simulation with FullMonte

High-performance, robustly verified Monte Carlo simulation with FullMonte

Macroscopic-imaging technique for subsurface quantification of near-infrared markers during surgery

GPU-accelerated mesh-based Monte Carlo photon transport simulations

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