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GPU-Based Fast Monte Carlo Simulation On the Chemical Stage of Water Radiolysis

Z Tian

Z Tian*, S Jiang , X Jia , The University of Texas Southwestern Medical Center, Dallas, TX


MO-DE-605-5 (Monday, July 31, 2017) 1:45 PM - 3:45 PM Room: 605

Purpose: Accurate simulation of water radiolysis is essential to understand the mechanisms of radiobiology and quantitatively test some hypotheses regarding radiobiological effects. However, it is time consuming to simulate the chemical stage, which is characterized by diffusion of the radiolytic molecules and their mutual chemical reactions. The main reason is the high code complexity to track the reactions among a large number of molecules through the entire stage. This project is to accelerate the simulation of the chemical stage to facilitate related studies.

Methods: We have implemented MC simulation of the chemical stage on GPU, employing step-by-step method and dynamic time step. A GPU-friendly parallelization strategy has been designed to address the highly correlated many-body simulation problem caused by the mutual and competitive reactions between the radiolytic molecules. To reduce code complexity, we searched for the potential reactants of a molecule only in its vicinity, realized on GPU using grid data approach.

Results: We have tested two cases, with a 750 keV electron and a 5 MeV proton incident in pure water respectively. The time-dependent yields of all the radiolytic species were used to evaluate the simulation accuracy. The relative differences between our simulation and the Geant4-DNA simulation were on average 5.3% and 4.4% for the two cases. Our package, executed on an Nvidia Titan black GPU card, completed the chemical stage simulation of the two cases within 599.2 s and 489.0 s. As compared with Geant4-DNA that was executed on an Intel i7-5500U CPU processor and needed 28.6 h and 26.8 h for the two cases using a single CPU core, our package achieved a speed-up factor of 171.1-197.2.

Conclusion: MC simulation of the chemical stage in water radiolysis can be substantially accelerated using the powerful parallelization capability of GPU when a large number of radiolytic molecules are involved.

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