CUDA-level performance with python-level productivity for Gaussian mixture model applications
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Massively parallel expectation maximization using graphics processing units
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Expectation Maximization (EM) algorithm is an iterative technique widely used in the fields of signal processing and data mining. We present a parallel implementation of EM for finding maximum likelihood estimates of parameters of Gaussian mixture models, designed for many-core architecture of Graphics Processing Units (GPU). The algorithm is implemented on NVIDIA's GPUs using CUDA, following the single instruction multiple threads model. In this paper, the emphasis is laid on exploiting the data parallelism with CUDA, thus accelerating the computations. CUDA implementation of EM is designed in such a way that the speed of computation of the algorithm scales up with the number of GPU cores. Experimental results confirm the scalability across cores. The results also show that CUDA implementation of EM when applied to an input of 230K for a 32-order mixture of 32-dimensional Gaussian model takes 264 msec on Quadro FX 5800 (NVIDIA 200 series) with 240 cores to complete one iteration which is about 164 times faster when compared to a naive single threaded C implementation on CPU.