The parallel evaluation of hidden Markov models on graphic processing units in supervised recognition

A parallel algorithm on graphic processing units is presented to evaluate an observation sequence on hidden Markov models quickly. The evaluation is compute-intensive when there are thousands of or more Markov models and there are many states in each model. The compute-load can be reduced by the proposed algorithm greatly, but the hardware needed by the algorithm is not so expensive as the other accelerating hardware and is used widely in PCs. In a supervised recognition task, all of the hidden Markov models must be sorted by their evaluation probability in descent order, one can select a proper model from the list quickly. A sorting network was implemented in the algorithm to run on graphic processing units to sort the models in parallel by their evaluation probability. The algorithm was tested on a NVIDIA 9800 GTX+ graphic processing units, experimental results showed the parallel algorithm can evaluate the probability of an observation sequence on hidden Markov models 10~100 times fast than the serial algorithm does on Pentium E5200 CPU.