Workload characteristics for iris matching algorithm: A case study

With the rapidly expanded biometric data collected by various sectors of government and industry for identification and verification purposes, how to manage and process such Big Data draws great concern. Even though modern processors are equipped with more cores and memory capacity, it still requires careful design in order to utilize the hardware resource effectively and the power consumption efficiently. This research addresses this issue by investigating the workload characteristics of biometric application. Taking Daugman´s iris matching algorithm, which has been proven to be the most reliable iris matching method, as a case study, we conduct performance profiling and binary instrumentation on the benchmark to capture its execution behavior. The results show that data loading and memory access incurs great performance overhead and motivates us to move the biometrics computation to highperformance architecture.