MRI: Acquisition of a Heterogeneous Computing Platform for Biometrics Research

该项目将获得一个异构高性能计算集群，旨在支持生物识别和识别技术的并行处理研究，以及广泛的工程研究学科。有效和高效地管理和分析大规模生物特征信息的操作能力是生物识别研究人员面临的主要挑战。新兴的计算元素，如多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对当前国家利益问题的新应用。该平台应从异构架构出发，实现高效、高性能；从功率意识和能量意识出发，实现高效。生物识别和高性能研究计算社区都将受益于异构高性能计算平台，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）的主要站点，该中心是由多所大学组成的NSF I/UCRC，该仪器在支持其附属机构的持续研究工作方面发挥了重要作用，因为这些研究朝着生物识别的高性能计算方面的更先进的研究方向发展。该仪器为研究人员提供了在异构计算平台上促进生物识别应用并行处理的能力。该系统提供合适的能力，以处理广泛的生物识别应用超出了那些目前可用的。此外，该设备还支持其他竞争性研究的竞争。该集群由中央处理单元（cpu）、图形处理单元（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，作为数据捕获前端。高性能计算社区承认，随着从单核处理器到多核/多核处理器的过渡，没有一个单一的处理元素可以实现生物识别应用（以及其他不同的应用）的最佳性能，因为通常程序的不同部分具有适合不同处理元素加速的不同并行特性。在生物识别应用程序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程序的不同区域映射到不同的硬件组件上，并将它们编排成一个整体，以便以有效和高效的方式产生结果。因此，为了获得最佳性能，需要使用计算元素的组合。

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.