Poole C M, Cornelius I, Trapp J V, Langton C M
Cancer Care Services, Royal Brisbane and Womens Hospital, Herston, Australia.
Australas Phys Eng Sci Med. 2012 Dec;35(4):497-502. doi: 10.1007/s13246-012-0167-8. Epub 2012 Nov 28.
Cloud computing allows for vast computational resources to be leveraged quickly and easily in bursts as and when required. Here we describe a technique that allows for Monte Carlo radiotherapy dose calculations to be performed using GEANT4 and executed in the cloud, with relative simulation cost and completion time evaluated as a function of machine count. As expected, simulation completion time decreases as 1/n for n parallel machines, and relative simulation cost is found to be optimal where n is a factor of the total simulation time in hours. Using the technique, we demonstrate the potential usefulness of cloud computing as a solution for rapid Monte Carlo simulation for radiotherapy dose calculation without the need for dedicated local computer hardware as a proof of principal.
云计算使大量计算资源能够在需要时快速、轻松地按需突发利用。在此，我们描述了一种技术，该技术允许使用GEANT4进行蒙特卡罗放射治疗剂量计算并在云端执行，同时将相对模拟成本和完成时间作为机器数量的函数进行评估。正如预期的那样，对于n个并行机器，模拟完成时间按1/n减少，并且发现相对模拟成本在n是总模拟时间（以小时为单位）的一个因数时是最优的。使用该技术，我们证明了云计算作为一种无需专用本地计算机硬件即可进行放射治疗剂量计算的快速蒙特卡罗模拟解决方案的潜在实用性，以此作为原理验证。
