Department of Medical Physics, Virgen Macarena Hospital, Seville, Spain.
Phys Med Biol. 2013 Apr 21;58(8):N125-33. doi: 10.1088/0031-9155/58/8/N125. Epub 2013 Mar 21.
This work presents CloudMC, a cloud computing application-developed in Windows Azure®, the platform of the Microsoft® cloud-for the parallelization of Monte Carlo simulations in a dynamic virtual cluster. CloudMC is a web application designed to be independent of the Monte Carlo code in which the simulations are based-the simulations just need to be of the form: input files → executable → output files. To study the performance of CloudMC in Windows Azure®, Monte Carlo simulations with penelope were performed on different instance (virtual machine) sizes, and for different number of instances. The instance size was found to have no effect on the simulation runtime. It was also found that the decrease in time with the number of instances followed Amdahl's law, with a slight deviation due to the increase in the fraction of non-parallelizable time with increasing number of instances. A simulation that would have required 30 h of CPU on a single instance was completed in 48.6 min when executed on 64 instances in parallel (speedup of 37 ×). Furthermore, the use of cloud computing for parallel computing offers some advantages over conventional clusters: high accessibility, scalability and pay per usage. Therefore, it is strongly believed that cloud computing will play an important role in making Monte Carlo dose calculation a reality in future clinical practice.
本文提出了 CloudMC，这是一个在 Windows Azure®（微软云平台）上开发的云计算应用程序，用于在动态虚拟集群中并行化蒙特卡罗模拟。CloudMC 是一个 Web 应用程序，旨在与模拟所基于的蒙特卡罗代码独立——模拟只需要采用以下形式：输入文件→可执行文件→输出文件。为了研究 CloudMC 在 Windows Azure®中的性能，我们在不同的实例（虚拟机）大小上对 penelope 进行了蒙特卡罗模拟，并对不同数量的实例进行了模拟。实例大小对模拟运行时间没有影响。还发现，随着实例数量的增加，时间的减少符合阿姆达尔定律，但由于随着实例数量的增加，不可并行化时间的比例增加，出现了轻微的偏差。在单个实例上运行 30 小时的模拟，在 64 个实例上并行执行时，仅需 48.6 分钟即可完成（加速比为 37×）。此外，云计算在并行计算方面的应用比传统集群具有一些优势：高可访问性、可扩展性和按使用付费。因此，我们坚信云计算将在未来的临床实践中使蒙特卡罗剂量计算成为现实方面发挥重要作用。
