论文标题
使用按需云基础架构进行蒙特卡洛建模光子组织相互作用
Monte Carlo modeling photon-tissue interaction using on-demand cloud infrastructure
论文作者
论文摘要
目的：这项工作推进了一种蒙特卡洛（MC）方法，以将电离辐射物理与光学物理相结合，这种方式被隐式设计用于部署，并具有最广泛的并行性和可移植性。方法：当前的工作更新了用于医学上的仿真（GAMOS）的Geant4体系结构的先前开发的光学传播插件。使用先前发布的脚本验证了Virtual-Machine（VM）和基于容器的实例，并证明了使用并行模拟的执行时间改进。提出了一种通过基于基于云的基础架构进行编程部署多个容器以实现并行执行的方法。结果：使用多层组织模型以及光学和X射线源输入来证明基于容器的GAMOS部署。例如，该模型分为154个模拟，这些模拟在4个服务器上同时在64个单独的容器上运行。结论：基于容器的模型提供了执行并非固有线程安全或GPU优化的应用程序的并行模拟的能力。在当前的演示中，与顺序执行相比，这最多减少了97％。该代码和示例可通过以下链接通过以下链接获得：https：//sites.dartmouth.edu/optmed/research-projects/monte-carlo-software/
Purpose: This work advances a Monte Carlo (MC) method to combine ionizing radiation physics with optical physics, in a manner which was implicitly designed for deployment with the most widely accessible parallelization and portability possible. Methods: The current work updates a previously developed optical propagation plugin for GEANT4 architecture for medically oriented simulations (GAMOS). Both virtual-machine (VM) and container based instances were validated using previously published scripts, and improvements in execution time using parallel simulations are demonstrated. A method to programmatically deploy multiple containers to achieve parallel execution using an on-demand cloud-based infrastructure is presented. Results: A container-based GAMOS deployment is demonstrated using a multi-layer tissue model and both optical and X-ray source inputs. As an example, the model was split into 154 simulations which were run simultaneously on 64 separate containers across 4 servers. Conclusions: The container-based model provides the ability to execute parallel simulations of applications which are not inherently thread-safe or GPU-optimized. In the current demonstration, this reduced the time by at most 97% compared to sequential execution. The code and examples are available through an interactive online interface through links at: https://sites.dartmouth.edu/optmed/research-projects/monte-carlo-software/
