PNNL

Abstract

This paper describes the use of high performance, massively parallel unstructured data analysis techniques to computationally extract human faces from streaming video data. The use of parallel high-throughput algorithms is essential to maximize the rate at which frames can be analyzed so large volumes of data can be evaluated to answer the question “Have we seen this person’s face before?” This paper will highlight the method used to achieve near-linear scalability according to number of processing cores for principle component analysis (PCA) on the Texas Advanced Computing Center (TACC) Ranger supercomputer. A global summation across processing core operation, accomplished via MPI_Reduce on Ranger, causes a slight deviation from linear scaling behavior. This mapping has been utilized in the past to enable a host of data centric analytic techniques such as neural networks, multi-dimensional scaling (MDS), signal processing and other methods to address, with massively parallel computers, problems of scientific interest in Physics, Mathematics, Biology and Chemistry.