HECURADeconstructing Clusters for High End Biometric Applications

HECURA解构高端生物识别应用的集群

• 批准号: 0621434
• 负责人: Douglas Thain
• 金额: --
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-03-01 至 2010-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0621434&HistoricalAwards=false
• 关键词: HECURADeconstructing; Clusters; End; Biometric; Applications

Today's user of scientific computing facilities has easy access to thousands of processors. However, this bounty of processing power has led to a data crisis. A conventional computing system often dispatches hundreds or thousands of jobs that simultaneously access a centralized server, which inevitably becomes a bottleneck. To support large data intensive applications, clusters must expose control of their internal storage and computing resources to an external scheduler that can make more informed placement decisions. This technique is called deconstructing clusters.This project attacks a particular data-intensive problem in high-end biometric research: the pair-wise comparison of hundreds of thousands of face images. The technique of deconstructing clusters will be used to parallelize the workload across large computing clusters. If successful, this project will reduce the time to develop and analyze a new biometric matching algorithm from years to days, thus improving the productivity of biometric researchers. The broader impact upon society will be an improvement in the accuracy and efficiency of biometric identification for commercial and national security. The software will be published in open source form in order to benefit other scientific computations with a similar pair-wise computation model.

今天的科学计算设备的用户很容易接触到成千上万的处理器。然而，这种海量的处理能力导致了数据危机。传统的计算系统通常会调度数百或数千个同时访问集中式服务器的作业，这不可避免地成为瓶颈。为了支持大型数据密集型应用程序，集群必须将对其内部存储和计算资源的控制公开给外部调度器，该调度器可以做出更明智的放置决策。这种技术被称为解构集群。这个项目解决了高端生物识别研究中一个特殊的数据密集型问题：对数十万张人脸图像进行成对比较。解构集群的技术将用于跨大型计算集群并行化工作负载。如果成功，该项目将把开发和分析新的生物识别匹配算法的时间从几年缩短到几天，从而提高生物识别研究人员的生产力。对社会的更广泛影响将是提高商业和国家安全的生物识别的准确性和效率。该软件将以开放源代码的形式发布，以使其他具有类似成对计算模型的科学计算受益。