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Most machine learning algorithms need to handle large data sets. This feature often leads to limitations on processing time and memory. The Expectation-Maximization (EM) is one of such algorithms, which is used to train one of the most commonly used parametric statistical models, the Gaussian Mixture Models (GMM). All steps of the algorithm are potentially parallelizable once they iterate over the entire data set. In this study, we propose a parallel implementation of EM for training GMM using CUDA. Experiments are performed with a UCI dataset and results show a speedup of 7 if compared to the sequential version. We have also carried out modifications to the code in order to provide better access to global memory and shared memory usage. We have achieved up to 56.4% of achieved occupancy, regardless the number of Gaussians considered in the set of experiments.

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Computational efforts for Alternative and Augmented Communication (AAC) must deal with two important issues, that slow down their spread: (1) the need for specific hardware and software in order to engage each different disability and (2) the different communication languages. This paper presents the PickNClick, a hardware/software apparatus that allows for ample AAC in Brazil. The PickNClick consists of a word and sentence prediction tool for Portuguese language to help people with cerebral palsy, in particular, spastic quadriplegic children. Experiments conducted presented good values concerning KSR metric and showed the software applicability cerebral palsy and spastic quadriplegic volunteer.