基于可重构微服务器的高能效指纹比对方法

Energy-Efficient Fingerprint Matching Based on Reconfigurable Micro Server

大规模指纹应用需要强大的后端指纹比对计算能力作为支撑.基于可重构微服务器(reconfigurable micro server,RMS)技术,提出一种软硬协同的高效指纹比对方法,该方法充分发挥可重构混合核心计算架构的优势,采用优化定制的硬件加速部件对指纹比对算法中的计算密集部分进行加速.复杂控制流和离散访存较多的算法部分则以软件形式在通用计算核心上高效执行.在单个RMS计算节点上完成了算法原型的实现并进行了详细测试.测试结果表明:单个RMS节点上的指纹比对性能约为105万次/秒,功耗仅为5W.与相关工作相比,该性能是单个X86集群节点的15.5倍;能效是X86集群节点的583倍,是基于Tesla C2075的GPU服务器的5.4倍.与单纯的FPGA平台相比,基于RMS技术的实现方法更具灵活性和可扩展性,是未来构建大规模指纹比对系统的一种高效的技术解决方案.

Large‐scale fingerprint based application needs high‐performance fingerprint matching backend system as a support .Based on reconfigurable micro server （RMS） technology ,we propose a software‐hardware cooperated fingerprint matching approach . Relying on the advantages of reconfigurable hybrid core computing architecture , our approach can accelerate the computing intensive part of fingerprint matching algorithm by using highly customized hardware accelerator and process the parts w hich contain complex control flow s and a large number of discrete memory accesses on general processing cores .Then ,we complete the implementation of algorithm prototype and the performance test on RMS computing node .The test result shows that ,single RMS node can achieve about 10 ,500 fingerprint matches per second with only 5 watts power consumption .Compared with related works ,the fingerprint matching performance of a single RMS computing node is 15 .5 times that of a single X86 cluster node .Its energy efficiency is 583 times of single X86 cluster node and 5 .4 times of Tesla C2075 based GPU server .Based on RMS technology ,our method is more flexible and extensible than FPGA platform .It is expected to become an effective technique solution for building large‐scale fingerprint matching system in the future .