Acquisition of a Beowulf Class Cluster Computer to Support Interdisciplinary Faculty Research and Graduate Student Training Using Numerically Intensive Statistical Modeling Methods

购买贝奥武夫级集群计算机，使用数值密集型统计建模方法支持跨学科教师研究和研究生培训

• 批准号: 0320284
• 负责人: Jeffrey Racine
• 金额: $ 11.4万
• 依托单位: Syracuse University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-08-01 至 2004-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0320284&HistoricalAwards=false
• 关键词: Acquisition; Beowulf; Class; Cluster; Computer

With support from a National Science Foundation Major Research Instrumentation award, Professor J.S. Racine, Senior Research Associate in the Center for Policy Research (CPR) of the Maxwell School at Syracuse University, will establish a 32-dual-processor-node Beowulf class cluster computer in CPR to support faculty research and graduate student training using numerically intensive statistical modeling methods. The Beowulf class cluster computer will provide supercomputer computing power at a relatively low cost using off-the-shelf hardware components, open source software, and a substantial degree of platform independence. Professor J. S. Racine will supervise the purchase, installation, and operation of the Beowulf class cluster computer, assisted by the CPR computer consultant, whose training in Beowulf cluster computers will be supported by this award. Racine anticipates that initially 9 faculty members and 20 to 30 graduate students will benefit from the purchase and implementation of this computer, and that these numbers will grow as other Maxwell School research faculty develop projects to exploit the Beowulf cluster computer's capabilities.Applied statistical research is rapidly evolving from a paradigm based upon parametric models coupled with inference, which relies on asymptotic approximations, to one based on nonparametric models and inference based on resampling. These large information-rich datasets are ripe for nonparametric analysis, but their computational burden all but prevents their application for such datasets. This also applies to methods that hold the promise for improved parametric modeling. These computational obstacles can be overcome by using a parallel computing environment, the most practical form of which is a Beowulf class cluster computer.CPR has at least nine faculty who either work with numerically intensive econometric methods and who have expertise in this area, or who routinely struggle with computational aspects of modeling large datasets. With this cluster computer, we will be able to teach them not only the theory of these methods but how to apply them to large, real world databases such as those they will work with after graduation.Broader Impacts: The Maxwell School of Citizenship and Public Affairs at Syracuse University, in which the Center for Policy Research is located, conducts high-quality social science research and trains graduate students for academic and research positions, as well as responsible government and non-governmental agency positions around the world. Should the project be funded, researchers will be able to leverage the cluster to apply cutting-edge statistical methods to numerous datasets housed by our center, all of which will result in improved policy analysis and enhanced understanding of empirical phenomena. Finally, software developed on the cluster will, of course, be made publicly available, thereby maximizing the broader impacts of the investment.We are extremely excited to have this resource available for use by both faculty and students in the upcoming year. Simply put, we will be able to apply the latest numerically intensive statistical methods to rich datasets which currently remain beyond our computational reach.

在国家科学基金会主要研究仪器奖的支持下，J.S. Racine是锡拉丘兹大学麦克斯韦学院政策研究中心（CPR）的高级研究员，他将在CPR中建立一个32个双处理器节点的Beowulf类集群计算机，以支持教师研究和研究生培训，使用数字密集型统计建模方法。Beowulf级集群计算机将以相对较低的成本提供超级计算机的计算能力，使用现成的硬件组件，开源软件和相当程度的平台独立性。J.S.教授Racine将在CPR计算机顾问的协助下监督Beowulf级集群计算机的购买、安装和操作，CPR计算机顾问在Beowulf集群计算机方面的培训将得到该奖项的支持。拉辛预计，最初9名教师和20至30名研究生将受益于购买和实施这台计算机，这些数字将随着其他麦克斯韦学校研究人员开发项目，以利用贝奥武夫集群计算机的能力而增长。应用统计研究正在迅速发展，从一个范例的基础上参数模型加上推理，这依赖于渐近近似，一个基于非参数模型和推理的基础上reservation。这些大型的信息丰富的数据集是成熟的非参数分析，但他们的计算负担，但阻止他们的应用程序，这样的数据集。这也适用于有望改进参数化建模的方法。这些计算障碍可以通过使用并行计算环境来克服，其中最实用的形式是Beowulf级集群计算机。CPR至少有九名教师，他们要么从事数值密集型计量经济学方法的工作，要么在这一领域拥有专业知识，要么经常与大型数据集建模的计算方面作斗争。有了这台集群计算机，我们不仅可以教他们这些方法的理论，还可以教他们如何将它们应用到大型的、真实的世界数据库中，比如他们毕业后将要使用的数据库。政策研究中心所在的锡拉丘兹大学麦克斯韦公民与公共事务学院，开展高质量的社会科学研究，并为学术和研究职位以及世界各地负责任的政府和非政府机构职位培训研究生。如果该项目获得资助，研究人员将能够利用该集群将尖端的统计方法应用于我们中心的众多数据集，所有这些都将改善政策分析并增强对经验现象的理解。最后，在集群上开发的软件当然将公开提供，从而最大限度地扩大投资的广泛影响。我们非常高兴能在未来一年内为教师和学生提供这一资源。简而言之，我们将能够将最新的数值密集型统计方法应用于目前超出我们计算范围的丰富数据集。