MRI: Acquisition of a Heterogeneous Computing Platform for Biometrics Research

MRI：收购用于生物识别研究的异构计算平台

• 批准号: 1626360
• 负责人: Chen Liu
• 金额: $ 39.74万
• 依托单位: Clarkson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1626360&HistoricalAwards=false
• 关键词: MRI; Acquisition; Heterogeneous; Computing; Platform

This project, acquiring a heterogeneous high-performance computing cluster, aims to support parallel processing research of biometrics and identification technology, as well as broad disciplines of engineering research. Operational capabilities of managing and analyzing large-scale biometric information in an effective and efficient manner constitutes a major challenge faced by researchers in advancing biometrics. Emerging computing elements, such as many-core processors and hardware coprocessors, play an essential role in achieving this goal. This project enables the proponents to investigate novel applications of emerging hardware technology to a problem of current national interest. The platform should achieve effectiveness with great performance from its heterogeneous architecture and efficiency with power-awareness and energy awareness. Both the biometrics and high-performance research computing community will gain from the heterogeneous high-performance computing platform that can employ various state-of-the-art parallel architectures for hardware acceleration of biometric applications. It can serve as a design reference for next-generation commercial and governmental biometric systems. Since this institution currently serves as the lead site of the Center for Identification Technology Research (CITeR), a multi-university NSF I/UCRC, the instrumentation serves as a great enabler in support of continued research efforts of its affiliates interests as these evolve towards more advanced research in high-performance computing aspects of biometrics. This instrumentation provides the capability for the researchers to contribute towards and advance the parallel processing of biometric applications on heterogeneous computing platforms. The system lends suitable capability for processing a wide range of biometrics applications beyond those currently available. Moreover, the equipment also supports efforts to compete for other competitive research.The cluster consist of Central Processing Units (CPUs), Graphics Processing Units (GPUs), Many-Integrated Core (MIC) co-processors, and Field-Programmable Gate Arrays (FPGAs), tightly integrated with a light field camera as a data-capturing front-end. The high-performance computing community acknowledges that with the transition from single-core processor to multi/ many-core processors, no one single processing element can achieve the best performance for biometrics applications (as well as other different applications) since often different parts of the program have different parallelism characteristics suitable for acceleration by different processing elements. Inherited in biometric applications a large degree of data parallelism exists that requires carefully mapping the different region of the biometric applications onto different hardware components and orchestrating them to function as whole, so as to produce results in an effective and efficient manner. So, in order to achieve the best performance, a combination of computing elements need to be used.

该项目获得了一个异构的高性能计算集群，旨在支持生物特征识别和身份识别技术的并行处理研究，以及广泛的工程研究学科。以有效和高效的方式管理和分析大规模生物特征信息的操作能力构成了研究人员在推进生物特征识别方面面临的主要挑战。新兴的计算元件，如众核处理器和硬件协处理器，在实现这一目标方面发挥着至关重要的作用。该项目使支持者能够研究新兴硬件技术在当前国家利益问题上的新应用。该平台应该通过其异构架构的出色性能和功率感知和能源感知的效率来实现有效性。生物识别和高性能研究计算社区都将从异构高性能计算平台中获益，该平台可以采用各种最先进的并行架构来实现生物识别应用的硬件加速。它可以作为下一代商业和政府生物识别系统的设计参考。由于该机构目前是识别技术研究中心（CITeR）（一个多大学NSF I/UCRC）的牵头机构，因此该仪器可以作为一个巨大的推动者，支持其附属机构的持续研究工作，因为这些研究工作正在朝着更先进的方向发展。生物识别技术的高性能计算方面的研究。该仪器为研究人员提供了在异构计算平台上促进和推进生物识别应用程序并行处理的能力。该系统提供了适当的能力，处理目前可用的生物测定应用程序之外的各种生物测定应用程序。该集群由中央处理器（CPU）、图形处理器（GPU）、多集成核心（MIC）协处理器和现场可编程门阵列（FPGA）组成，并与光场相机紧密集成，作为数据采集前端。高性能计算社区承认，随着从单核处理器到多核/众核处理器的转变，没有一个单个处理元件可以实现生物测定应用（以及其他不同应用）的最佳性能，因为程序的不同部分通常具有适合于由不同处理元件加速的不同并行特性。在生物识别应用中继承了很大程度的数据并行性，这需要仔细地将生物识别应用的不同区域映射到不同的硬件组件上，并将它们编排为整体运行，以便以有效和高效的方式产生结果。因此，为了实现最佳性能，需要使用计算元素的组合。