Trellis: A Domain-Specific Language for Hidden Markov Models with Sparse Transitions

Abstract

Hidden Markov models (HMMs) are frequently used in areas such as speech recognition and bioinformatics. However, implementing HMM algorithms correctly and efficiently is time-consuming and error-prone. Specifically, using model-specific knowledge to improve performance, such as sparsity in the transition probability matrix, ties the implementation to a particular model, making it harder to modify. Previous work has introduced high-level frameworks for defining HMMs, thus lifting the burden of efficiently implementing HMM algorithms from the user. However, existing tools are ill-suited for sparse HMMs with many states. This paper introduces Trellis, a domain-specific language for succinctly defining sparse HMMs that use GPU acceleration to achieve high performance. We show that Trellis outperforms previous work and is on par with a hand-written CUDA kernel implementation for a particular sparse HMM.