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 maximum-a-posteriori (MAP) algorithm. We define associative elements and operators to pose these inference problems as all-prefix-sums 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).

中文翻译：

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隐马尔可夫模型中推理的时间并行化

本文介绍了隐马尔可夫模型 (HMM) 中推理并行化的算法。特别是，我们提出了一种并行的前向后向滤波和平滑算法以及一种并行的维特比型最大后验 (MAP) 算法。我们定义关联元素和运算符将这些推理问题作为全前缀和计算，并使用并行扫描算法将它们并行化。所提出算法的优点是它们在具有长时间范围的 HMM 推理问题中具有计算效率。我们根据经验将所提出方法的性能与高度并行图形处理单元 (GPU) 上的经典方法进行了比较。