Performance evaluation of the Intel Xeon Phi manycore architecture using parallel video-based driver assistance algorithms

Computational-intensive algorithms are often realized with dedicated or customized hardware architectures suffering from high development costs and low flexibility thereafter. Instead, modern multicore and manycore processors can execute a diversity of software applications (e.g, driver assistance systems) written in portable high-level programming languages resulting in less porting effort at lower costs for power-consumption tolerant fields. For instance, the Intel Xeon Phi manycore processor featuring 61 cores offers not only a high theoretical peak performance but also a supportive tool chain for the software development in high-level programming languages. In contrast to traditional general-purpose multicore processors, this manycore architecture, however, exhibits different processor characteristics, inter-core communication topologies, and instruction sets. In this paper, we introduce the parallel implementation of a histogram of oriented gradients algorithm for pedestrian detection. Using a parallel semi-global matching algorithm as well, serving as an additional driver assistance algorithm, we present an in-depth performance analysis case study on the Intel Xeon Phi and also note distinct characteristics of this target platform. To allow a fair comparison, we not only rate an Intel Xeon 16-core general-purpose processor, but also present a platform comparison to customized hardware architectures.