  Finite mixture models are powerful tools for modelling and analyzing
heterogeneous data. Parameter estimation is typically carried out using maximum
likelihood estimation via the Expectation-Maximization (EM) algorithm.
Recently, the adoption of flexible distributions as component densities has
become increasingly popular. Often, the EM algorithm for these models involves
complicated expressions that are time-consuming to evaluate numerically. In
this paper, we describe a parallel implementation of the EM-algorithm suitable
for both single-threaded and multi-threaded processors and for both single
machine and multiple-node systems. Numerical experiments are performed to
demonstrate the potential performance gain n different settings. Comparison is
also made across two commonly used platforms - R and MATLAB. For illustration,
a fairly general mixture model is used in the comparison.
