Parallel Performance Studies for a Maximum Likelihood Estimation Problem Using TAO

Abstract

In this report, we present an application of parallel computing to an estimation procedure in statistics. The method of maximum likelihood estimation (MLE) is based on the ability to perform maximizations of probability functions. In practice, this work is often performed by computer with numerical methods, and may be time consuming for some likelihood functions. We consider one such likelihood function based on the Finite Mixture Multinomial distribution. We perform estimation for this problem in parallel using the Toolkit for Advanced Optimization (TAO) software library. The computations are performed on a distributed-memory cluster with InfiniBand interconnect in the High Performance Computing Facility at University of Maryland, Baltimore County (UMBC). We study how the resource requirements change as problem sizes vary, and demonstrate that scaling the number of processes for larger problems decreases wall clock time significantly.