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首页> 外文会议>2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum >Evolving N-Body Simulations to Determine the Origin and Structure of the Milky Way Galaxy's Halo Using Volunteer Computing
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Evolving N-Body Simulations to Determine the Origin and Structure of the Milky Way Galaxy's Halo Using Volunteer Computing

机译：不断发展的N体仿真，使用志愿者计算确定银河系光晕的起源和结构

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This work describes research done by the MilkyWay@Home project to use N-Body simulations to model the formation of the Milky Way Galaxy's halo. While there have been previous efforts to use N-Body simulations to perform astronomical modeling, to our knowledge this is the first to use evolutionary algorithms to discover the initial parameters to the N-Body simulations so that they accurately model astronomical data. Performing a single 32,000 body simulation can take up to 200 hours on a typical processor, with an average of 15 hours. As optimizing the input parameters to these N-Body simulations typically takes at least 30,000 or more simulations, this work is made possible by utilizing the computing power of the 35,000 volunteered hosts at the MilkyWay@Home project, which are currently providing around 800 teraFLOPS. This work also describes improvements to an open-source framework for generic distributed optimization (FGDO), which provide more efficient validation in performing these evolutionary algorithms in conjunction the Berkeley Open Infrastructure for Network Computing (BOINC).
机译：这项工作描述了MilkyWay @ Home项目完成的研究，该研究使用N-Body模拟来模拟银河系星系的光环形成。尽管以前一直在尝试使用N身体模拟来执行天文学建模，但据我们所知，这是第一个使用进化算法来发现N身体模拟的初始参数，以便它们可以准确地对天文数据进行建模的方法。在一个典型的处理器上执行一次32,000个人体模拟最多可能需要200个小时，平均需要15个小时。由于优化这些N体仿真的输入参数通常需要至少30,000或更多的仿真，因此可以利用MilkyWay @ Home项目中的35,000名自愿宿主的计算能力来完成这项工作，该项目目前提供约800 teraFLOPS。这项工作还描述了通用分布式优化（FGDO）开源框架的改进，该框架结合伯克利网络计算开放基础架构（BOINC）在执行这些进化算法时提供了更有效的验证。

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