Workload characterization of biometric applications on Pentium 4 microarchitecture

Biometric computing is a technique that uses physiological and behavioral characteristics of persons to identify and authenticate individuals. Due to the increasing demand on security, privacy and anti-terrorism, biometric applications represent the rapidly growing computing workloads. However, very few results on the execution characteristics of these applications on the state-of-the-art microprocessor and memory systems have been published so far. This paper proposes a suite of biometric applications and reports the results of a biometric workload characterization effort, focusing on various architecture features. To understand the impacts and implications of biometric workloads on the processor and memory architecture design, we contrast the characteristics of biometric workloads and the widely used SPEC 2000 integer benchmarks. Our experiments show that biometric applications typically show small instruction footprint that can fit in the L1 instruction cache. The loads and stores account for more than 50% of the dynamic instructions. This indicates that biometric applications are data-centric in nature. Although biometric applications work across large-scale datasets to identify matched patterns, the active working sets of these workloads are usually small. As a result, prefetching and large L2 cache effectively handle the data footprints of a majority of the studied benchmarks. Branch misprediction rate is less than 4% on all studied workloads. The IPC of the studied benchmarks ranges from 0.13 to 0.77 indicates that out-of-order superscalar execution is not quite efficient. The developed biometric benchmark suite (BMW) and input data sets are freely available and can be downloaded from http://www.ideal.ece.ufl.edu/BMW.