Fast Temporal Path Localization on Graphs via Multiscale Viterbi Decoding

This paper presents algorithms for the parallelization of inference in hidden Markov models (HMMs). In particular, we propose a parallel forward-backward type of filtering and smoothing algorithm as well as a parallel Viterbi-type maximuma-posteriori (MAP) algorithm. We define associative elements and operators to pose these inference problems as all-prefixsums computations and parallelize them using the parallel-scan algorithm. The advantage of the proposed algorithms is that they are computationally efficient in HMM inference problems with long time horizons. We empirically compare the performance of the proposed methods to classical methods on a highly parallel graphics processing unit (GPU).

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Fast Temporal Path Localization on Graphs via Multiscale Viterbi Decoding

Cited by 3 publications

References 54 publications

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