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首页> 外文会议>IEEE International Symposium on Parallel and Distributed Processing >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身体模拟，以确定银河系使用志愿者计算的原因和结构

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This work describes research done by the Milky-Way@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 Milky-Way@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Milky-Way @ Home Project进行的研究，用于使用N-Surd Simulations来模拟Milky Way Galaxy Halo的形成。虽然以前的努力使用N-Body模拟来执行天文建模，但对于我们的知识，这是第一个使用进化算法来发现N身体模拟的初始参数，使得它们准确地模拟天文数据。执行单个32,000个车身仿真可能在典型的处理器上花费高达200小时，平均为15小时。作为优化这些n身体模拟的输入参数通常需要至少30,000个或更多的模拟，这项工作是通过利用35,000名志愿主机的计算能力，目前正在提供约800左右的牛奶网@ Home Project的计算能力Teraflops。这项工作还描述了对通用分布式优化（FGDO）的开源框架的改进，这在执行这些进化算法中为网络计算（BOINC）的伯克利开放基础设施进行了更有效的验证。

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