New Results on an Improved Parallel EM Algorithm for Estimating Generalized Latent Variable Models

The second generation of a parallel algorithm for generalized latent variable models, including MIRT models and extensions, on the basis of the general diagnostic model (GDM) is presented. This new development further improves the performance of the parallel-E parallel-M algorithm presented in an earlier report by means of additional computational improvements that produce even larger gains in performance. The additional gain achieved by this second-generation parallel algorithm reaches factor 20 for several of the examples reported with a sixfold gain based on the first generation. The estimation of a multidimensional IRT model for large-scale data may show a larger reduction in runtime compared to a multiple-group model which has a structure that is more conducive to parallel processing of the E-step. Multiple population models can be arranged such that the parallelism directly exploits the ability to estimate multiple latent variable distributions separately in independent threads of the algorithm.