Accelerating Monte Carlo simulations of photon transport in a voxelized geometry using a massively parallel graphics processing unit

Badal, Andreu; Badano, Aldo

doi:10.1118/1.3231824

Cited by 282 publications

(250 citation statements)

References 16 publications

(5 reference statements)

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“…The MC-GPU package is explained in detail in Ref. 40 and is publicly available via the U. S. Food and Drug Administration, Center for Devices and Radiological Health (http://code.google.com/p/mcgpu/). The code employs interaction models and cross sections from PENELOPE 2006, 48 a general-purpose MC simulation system for electron-photon transport, and achieves a 15-to 30-fold acceleration over computation on a single CPU.…”

Section: Iia Gpu-accelerated Monte Carlo Simulation Platformmentioning

confidence: 99%

“…The simulation engine was based on the MC-GPU v1.1 package, 40 which exploits the capacity for parallel computing in modern GPUs for efficient implementation of MC x-ray photon tracking in a voxelized object geometry. The MC-GPU package is explained in detail in Ref.…”

Section: Iia Gpu-accelerated Monte Carlo Simulation Platformmentioning

confidence: 99%

“…The work reported below was performed using a GPU-accelerated MC implementation based on publicly available software library (MC-GPU) recently reported to offer a 27-fold speedup in simulation time over a single CPU. 40 CBCT system design for a particular application and development of optimal scatter correction methods stand to benefit from: (i) understanding the nature and form of x-ray scatter components (i.e., incoherent, coherent, and multiple scatter) contributing to the total scatter; (ii) the extent to which scatter rejection with antiscatter grids benefits image quality; and (iii) the effect of system geometry (e.g., source-to-object and object-to-detector distance) on the magnitude, nature, and spatial distribution of x-ray scatter and its components. Neitzel 41 provided an insightful analytical treatment of the question of system geometry (relative to antiscatter grids) in limiting x-ray scatter, concluding generally that systems with a short object-to-detector distance (i.e., a small "air gap") benefit most from use of an antiscatter grid, whereas systems that can accommodate a large air gap (i.e., a large objectto-detector distance) benefit foremost from the extended geometry in rejecting x-ray scatter, and a grid may be of little use (or actually detrimental, due to absorption of primary) to overall image quality per unit dose.…”

Section: Introductionmentioning

confidence: 99%

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Monte Carlo study of the effects of system geometry and antiscatter grids on cone‐beam CT scatter distributions

et al. 2013

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Purpose:The proliferation of cone-beam CT (CBCT) has created interest in performance optimization, with x-ray scatter identified among the main limitations to image quality. CBCT often contends with elevated scatter, but the wide variety of imaging geometry in different CBCT configurations suggests that not all configurations are affected to the same extent. Graphics processing unit (GPU) accelerated Monte Carlo (MC) simulations are employed over a range of imaging geometries to elucidate the factors governing scatter characteristics, efficacy of antiscatter grids, guide system design, and augment development of scatter correction. Methods: A MC x-ray simulator implemented on GPU was accelerated by inclusion of variance reduction techniques (interaction splitting, forced scattering, and forced detection) and extended to include x-ray spectra and analytical models of antiscatter grids and flat-panel detectors. The simulator was applied to small animal (SA), musculoskeletal (MSK) extremity, otolaryngology (Head), breast, interventional C-arm, and on-board (kilovoltage) linear accelerator (Linac) imaging, with an axis-todetector distance (ADD) of 5, 12, 22, 32, 60, and 50 cm, respectively. Each configuration was modeled with and without an antiscatter grid and with (i) an elliptical cylinder varying 70-280 mm in major axis; and (ii) digital murine and anthropomorphic models. The effects of scatter were evaluated in terms of the angular distribution of scatter incident upon the detector, scatter-to-primary ratio (SPR), artifact magnitude, contrast, contrast-to-noise ratio (CNR), and visual assessment. Results: Variance reduction yielded improvements in MC simulation efficiency ranging from ∼17-fold (for SA CBCT) to ∼35-fold (for Head and C-arm), with the most significant acceleration due to interaction splitting (∼6 to ∼10-fold increase in efficiency). The benefit of a more extended geometry was evident by virtue of a larger air gap-e.g., for a 16 cm diameter object, the SPR reduced from 1.5 for ADD = 12 cm (MSK geometry) to 1.1 for ADD = 22 cm (Head) and to 0.5 for ADD = 60 cm (C-arm). Grid efficiency was higher for configurations with shorter air gap due to a broader angular distribution of scattered photons-e.g., scatter rejection factor ∼0.8 for MSK geometry versus ∼0.65 for C-arm. Grids reduced cupping for all configurations but had limited improvement on scatterinduced streaks and resulted in a loss of CNR for the SA, Breast, and C-arm. Relative contribution of forward-directed scatter increased with a grid (e.g., Rayleigh scatter fraction increasing from ∼0.15 without a grid to ∼0.25 with a grid for the MSK configuration), resulting in scatter distributions with greater spatial variation (the form of which depended on grid orientation). Conclusions: A fast MC simulator combining GPU acceleration with variance reduction provided a systematic examination of a range of CBCT configurations in relation to scatter, highlighting the magnitude and spatial uniformity of individual scatter components, illustrating trad...

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Section: Iia Gpu-accelerated Monte Carlo Simulation Platformmentioning

confidence: 99%

Section: Iia Gpu-accelerated Monte Carlo Simulation Platformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Monte Carlo study of the effects of system geometry and antiscatter grids on cone‐beam CT scatter distributions

et al. 2013

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“…They tested the SHOC and Rodinia benchmark suites as synthetic kernels, NAMD [Phillips et al 2005] and MC-GPU [Badal and Badano 2009] as real world applications in science and engineering, and the HPL benchmark [Petitet 2004] as a widely used implementation of Linpack [Dongarra et al 2003]. They measured the performance both in virtualization using Amazon EC2 CGIs and in a native envi-ronment using their own cluster.…”

Section: Methods Supporting a Single Vmmentioning

confidence: 99%

GPU Virtualization and Scheduling Methods

2017

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The integration of graphics processing units (GPUs) on high-end compute nodes has established a new accelerator-based heterogeneous computing model, which now permeates high performance computing. The same paradigm nevertheless has limited adoption in cloud computing or other large-scale distributed computing paradigms. Heterogeneous computing with GPUs can benefit the Cloud by reducing operational costs and improving resource and energy efficiency. However, such a paradigm shift would require effective methods for virtualizing GPUs, as well as other accelerators. In this survey paper, we present an extensive and in-depth survey of GPU virtualization techniques and their scheduling methods. We review a wide range of virtualization techniques implemented at the GPU library, driver, and hardware levels. Furthermore, we review GPU scheduling methods that address performance and fairness issues between multiple virtual machines sharing GPUs. We believe that our survey delivers a perspective on the challenges and opportunities for virtualization of heterogeneous computing environments.

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“…Alerstam et al [16] then published on development of open-source GPU software designed to accelerate the popular Monte Carlo Multi-Layered (MCML) package [9]. Other groups proposed use of GPUs to perform specialized Monte Carlo simulation runs, for ionizing radiation transport [17], ultrasoundmodulated light [18], fluorescence generation and detection [19], and fiber-based diffuse reflectance spectroscopy [1]. Doronin and Meglinski [20] reported on online, GPUaccelerated MC simulations.…”

Section: Introductionmentioning

confidence: 99%

Accelerated rescaling of single Monte Carlo simulation runs with the Graphics Processing Unit (GPU)

Yang

Choi

2013

Biomed. Opt. Express

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To interpret fiber-based and camera-based measurements of remitted light from biological tissues, researchers typically use analytical models, such as the diffusion approximation to light transport theory, or stochastic models, such as Monte Carlo modeling. To achieve rapid (ideally real-time) measurement of tissue optical properties, especially in clinical situations, there is a critical need to accelerate Monte Carlo simulation runs. In this manuscript, we report on our approach using the Graphics Processing Unit (GPU) to accelerate rescaling of single Monte Carlo runs to calculate rapidly diffuse reflectance values for different sets of tissue optical properties. We selected MATLAB to enable non-specialists in C and CUDA-based programming to use the generated open-source code. We developed a software package with four abstraction layers. To calculate a set of diffuse reflectance values from a simulated tissue with homogeneous optical properties, our rescaling GPU-based approach achieves a reduction in computation time of several orders of magnitude as compared to other GPU-based approaches. Specifically, our GPU-based approach generated a diffuse reflectance value in 0.08ms. The transfer time from CPU to GPU memory currently is a limiting factor with GPU-based calculations. However, for calculation of multiple diffuse reflectance values, our GPUbased approach still can lead to processing that is ~3400 times faster than other GPU-based approaches. -based inverse model for extracting optical properties from tissue-simulating phantoms using diffuse reflectance spectroscopy," J.

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Accelerating Monte Carlo simulations of photon transport in a voxelized geometry using a massively parallel graphics processing unit

Cited by 282 publications

References 16 publications

Monte Carlo study of the effects of system geometry and antiscatter grids on cone‐beam CT scatter distributions

Monte Carlo study of the effects of system geometry and antiscatter grids on cone‐beam CT scatter distributions

GPU Virtualization and Scheduling Methods

Accelerated rescaling of single Monte Carlo simulation runs with the Graphics Processing Unit (GPU)

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