MRI: Acquisition of a Heterogeneous Computing Platform for Biometrics Research

该项目获得了一个异构型高性能计算集群，旨在支持生物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。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在当前国家利益问题上的新应用。该平台应该通过其异类架构和效率以及功率感知和能源感知来实现高效和出色的性能。生物识别和高性能研究计算社区都将受益于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的异构型高性能计算平台。它可以作为下一代商业和政府生物识别系统的设计参考。由于该机构目前是多所大学的NSF I/UCRC身份识别技术研究中心(CITER)的牵头站点，该仪器为支持其附属机构的继续研究努力起到了极大的推动作用，因为这些利益集团正在朝着生物识别的高性能计算方面的更高级研究发展。这种工具为研究人员提供了在异质计算平台上促进和推进生物识别应用程序的并行处理的能力。该系统为处理目前可用的各种生物识别应用程序提供了适当的能力。此外，该设备还支持竞争其他竞争性研究的努力。集群由中央处理单元(CPU)、图形处理单元(GPU)、多集成内核(MIC)协处理器和现场可编程门阵列(FGA)组成，与作为数据捕获前端的光场摄像机紧密集成。高性能计算界承认，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为生物识别应用程序(以及其他不同的应用程序)实现最佳性能，因为程序的不同部分往往具有不同的并行特性，适合不同的处理元素进行加速。在生物测定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测定应用程序的不同区域映射到不同的硬件组件上，并将它们作为整体来协调，以便以有效和高效的方式产生结果。因此，为了达到最佳性能，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.