论文标题
使用图形处理单元的隐藏马尔可夫模型拟合的1000倍加速度，并应用于非沃尔克尼克分类
A 1000-fold Acceleration of Hidden Markov Model Fitting using Graphical Processing Units, with application to Nonvolcanic Tremor Classification
论文作者
论文摘要
隐藏的马尔可夫模型（HMM）是时间序列数据的通用模型，因为它们具有灵活性和优雅，因此广泛使用了整个科学。但是，拟合的HMM通常可以是计算要求且耗时的，尤其是当隐藏状态的数量较大或马尔可夫链本身很长时。在这里，我们介绍了一种新的图形处理单元（GPU）算法，旨在适合长链HMM，将我们的方法应用于Wang等人（2018）开发的非volcanic震颤事件的HMM。即使在适度的GPU上，我们的实现也会导致标准单个处理器算法的速度提高1000倍，从而使与模型参数相关的不确定性的完整推断。考虑到大量观测值和中等状态空间（<80个带有当前硬件的状态），HMM模型的预计会有所改善。我们讨论了该模型，GPU架构和算法，并在日本Shikoku地区的震颤数据集上报告该方法的性能。
Hidden Markov models (HMMs) are general purpose models for time-series data widely used across the sciences because of their flexibility and elegance. However fitting HMMs can often be computationally demanding and time consuming, particularly when the the number of hidden states is large or the Markov chain itself is long. Here we introduce a new Graphical Processing Unit (GPU) based algorithm designed to fit long chain HMMs, applying our approach to an HMM for nonvolcanic tremor events developed by Wang et al.(2018). Even on a modest GPU, our implementation resulted in a 1000-fold increase in speed over the standard single processor algorithm, allowing a full Bayesian inference of uncertainty related to model parameters. Similar improvements would be expected for HMM models given large number of observations and moderate state spaces (<80 states with current hardware). We discuss the model, general GPU architecture and algorithms and report performance of the method on a tremor dataset from the Shikoku region, Japan.
