A unified spatio-temporal parallelization framework for accelerated Monte Carlo radiobiological modeling of electron tracks and subsequent radiation chemistry

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• 作者：Georgios Kalantzis^a ; ^{gkalan@gmail.com} ; Dimitrios Emfietzoglou^b ; Panagiotis Hadjidoukas^c
• 关键词：Grid computing ; Amazon EC2 ; Particle propagation ; Smoluchowski reaction&ndash ; diffusion ; Electron track structure ; Radiation chemistry
• 刊名：Computer Physics Communications
• 出版年：2012
• 期刊代码：30_00104655
• 类别：cp
• 出版时间：August, 2012
• 卷：183
• 期：8
• 页码：1683-1695
• 文件大小：996 K

摘要

Monte Carlo (MC) nano-scale modeling of the cellular damage is desirable but most times is prohibitive for large scaled systems due to their intensive computational cost. In this study a parallelized computational framework is presented, for accelerated MC simulations of both particle propagation and subsequent radiation chemistry at the subcellular level. Given the inherent parallelism of the electron tracks, the physical stage was 鈥渆mbarrassingly parallelized鈥?into a number of independent tasks. For the chemical stage, the diffusion-reaction of the radical species was simulated with a time-driven kinetic Monte Carlo algorithm (KMC) based on the Smoluchowski formalism and the parallelization was realized by employing a spatio-temporal linked-list cell method based on a spatial subdivision with a uniform grid. The evaluation of our method was established on two metrics: speedup and efficiency. The results indicated a linear speedup ratio for the physical stage and a linear latency for shared- versus a distributed-memory system with a maximum of per electron track. For the chemical stage, a series of simulations were performed to show how the execution time per step was scaling with respect to the number of radical species and a 5.7脳 speedup was achieved when a larger number of reactants were simulated and eight processors were employed. The simulations were deployed on the Amazon EC2 infrastructure. It is also elucidated how the overhead started becoming significant as the number of reactant species decrease relative to the number of processors. The method reported here lays the methodological foundations for accelerated MC simulations and allows envisaging a future use for large-scale radiobiological modeling of multi-cellular systems involved into a clinical scenario.