GPU Acceleration of Monte Carlo Simulation at the Cellular and DNA Levels

Okada, Shogo; Murakami, K.; Amako, K.; Sasaki, Takashi; Incerti, S.; Karamitros, Mathieu; Henderson, N.; Gerritsen, Margot; Asai, M.; Dotti, A.

doi:10.1007/978-3-319-23024-5_29

Cited by 4 publications

(4 citation statements)

References 10 publications

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“…Treating a physical interaction, such as ionization and excitation, as a discrete process allows the spatial accuracy to be preserved. MPEXS‐DNA adopts the same physical interactions that are available in the Geant4‐DNA extension of Geant4 (version 10.02 p03) . Charged particles handled in MPEXS‐DNA consist of electrons, protons, neutral hydrogen atoms, helium atoms with three charged states (He 0 , He + , He ++ ), and ions (Li, Be, B, C, N, O, Si, Fe).…”

Section: Methodsmentioning

confidence: 99%

“…MPEXS-DNA adopts the same physical interactions that are available in the Geant4-DNA extension of Geant4 (version 10.02 p03). 11,12 Charged particles handled in MPEXS-DNA consist of electrons, protons, neutral hydrogen atoms, helium atoms with three charged states (He 0 , He + , He ++ ), and ions (Li, Be, B, C, N, O, Si, Fe). Physical interactions for each particle, corresponding class names of Geant4-DNA processes, and applicable energy ranges are shown in Tables I-VI.…”

Section: C1 the Physical Stagementioning

confidence: 99%

“…at KEK in Japan has proposed to migrate Geant4‐DNA to graphics processing units (GPUs). They addressed the porting of Geant4‐DNA modeling of physical interactions in liquid water to GPUs, reaching almost two orders of magnitude in performance gain . These developments have now been extended in order to accelerate very significantly the simulation of water radiolysis, a key requirement for the fast simulation of DNA damage induced indirectly by molecular species.…”

Section: Introductionmentioning

confidence: 99%

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MPEXS‐DNA, a new GPU‐based Monte Carlo simulator for track structures and radiation chemistry at subcellular scale

et al. 2019

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PurposeTrack structure simulation codes can accurately reproduce the stochastic nature of particle–matter interactions in order to evaluate quantitatively radiation damage in biological cells such as DNA strand breaks and base damage. Such simulations handle large numbers of secondary charged particles and molecular species created in the irradiated medium. Every particle and molecular species are tracked step‐by‐step using a Monte Carlo method to calculate energy loss patterns and spatial distributions of molecular species inside a cell nucleus with high spatial accuracy. The Geant4‐DNA extension of the Geant4 general‐purpose Monte Carlo simulation toolkit allows for such track structure simulations and can be run on CPUs. However, long execution times have been observed for the simulation of DNA damage in cells. We present in this work an improvement of the computing performance of such simulations using ultraparallel processing on a graphical processing unit (GPU).MethodsA new Monte Carlo simulator named MPEXS‐DNA, allowing high computing performance by using a GPU, has been developed for track structure and radiolysis simulations at the subcellular scale. MPEXS‐DNA physics and chemical processes are based on Geant4‐DNA processes available in Geant4 version 10.02 p03. We have reimplemented the Geant4‐DNA process codes of the physics stage (electromagnetic processes of charged particles) and the chemical stage (diffusion and chemical reactions for molecular species) for microdosimetry simulation by using the CUDA language. MPEXS‐DNA can calculate a distribution of energy loss in the irradiated medium caused by charged particles and also simulate production, diffusion, and chemical interactions of molecular species from water radiolysis to quantitatively assess initial damage to DNA. The validation of MPEXS‐DNA physics and chemical simulations was performed by comparing various types of distributions, namely the radial dose distributions for the physics stage, and the G‐value profiles for each chemical product and their linear energy transfer dependency for the chemical stage, to existing experimental data and simulation results obtained by other simulation codes, including PARTRAC.ResultsFor physics validation, radial dose distributions calculated by MPEXS‐DNA are consistent with experimental data and numerical simulations. For chemistry validation, MPEXS‐DNA can also reproduce G‐value profiles for each molecular species with the same tendency as existing experimental data. MPEXS‐DNA also agrees with simulations by PARTRAC reasonably well. However, we have confirmed that there are slight discrepancies in G‐value profiles calculated by MPEXS‐DNA for molecular species such as H2 and H2O2 when compared to experimental data and PARTRAC simulations. The differences in G‐value profiles between MPEXS‐DNA and PARTRAC are caused by the different chemical reactions considered. MPEXS‐DNA can drastically boost the computing performance of track structure and radiolysis simulations. By using NVIDIA's GPU devices adopting the...

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Section: Methodsmentioning

confidence: 99%

Section: C1 the Physical Stagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MPEXS‐DNA, a new GPU‐based Monte Carlo simulator for track structures and radiation chemistry at subcellular scale

et al. 2019

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“…A solution to overcome this problem of computing time for PIXE tomography simulation in the future could be to develop this application using a GPU architecture. This type of development has recently been carried out in the frame of the Geant4-DNA project [30]. It keeps the accuracy of the Geant4 simulations whilst speeding up simulations with impressive performance gain (about 250 times faster).…”

Section: Executionmentioning

confidence: 99%

A Geant4 simulation for three-dimensional proton imaging of microscopic samples

Michelet

Yang

et al. 2019

Physica Medica

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In these experiments, the samples are dehydrated for under vacuum analysis. In situ quantification of nanoparticles has been carried out at CENBG in the frame of nanotoxicology studies, on cells and small organisms used as biological models, especially on Caenorhabditis elegans (C. elegans) nematodes. Tomography experiments reveal the distribution of mass density and chemical content (in g.cm-3) within the analyzed volume. These density values are obtained using an inversion algorithm. To investigate the effect of this data reduction process, we defined different numerical phantoms, including a (dehydrated) C. elegans phantom whose geometry and density were derived from experimental data. A Monte Carlo simulation based on the Geant4 toolkit was developed. Using different simulation and reconstruction conditions, we compared the resulting tomographic images to the initial numerical reference phantom. A study of the relative error between the reconstructed and the reference images lead to the result that 20 protons per shot can be considered as an optimal number for 3D STIM imaging. Preliminary results for PIXE tomography are also presented, showing the interest of such numerical phantoms to produce reference data for future studies on X-ray signal attenuation in thick samples.

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