HECURADeconstructing Clusters for High End Biometric Applications

今天的科学计算设施的用户可以很容易地访问数千个处理器。然而，这种慷慨的处理能力导致了数据危机。传统的计算系统经常分派同时访问集中式服务器的数百或数千个作业，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，集群必须将其内部存储和计算资源的控制权公开给外部调度程序，以便做出更明智的放置决策。 这项技术被称为解构集群，它解决了高端生物识别研究中的一个数据密集型问题：数十万张人脸图像的成对比较。将使用解构集群的技术来跨大型计算集群并行处理工作量。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生物识别的准确性和效率。该软件将以开源形式发布，以使具有类似成对计算模型的其他科学计算受益。

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.