High-Performance Tensor Formulation of the Viterbi Algorithm for Hidden Semi-Markov Models

2026-09-14

AI Summary

本文针对HSMM解码的大规模应用问题，提出了一种基于张量的Viterbi算法实现方式，并在CPU和GPU上进行了优化，显著提高了计算效率。

Abstract

Hidden Semi-Markov Models (HSMMs) are fundamental probabilistic models widely adopted across diverse domains, from computational biology to finance and signal processing. The Viterbi algorithm decodes the most likely state sequence given an HSMM and can be applied iteratively for ab initio model learning. However, existing Viterbi implementations remain sequential, and GPU-accelerated solutions are entirely absent, making HSMM decoding impractical for large-scale workloads. We present a tensor-based formulation of the Viterbi algorithm for HSMMs, restructuring the inner loops into tensor operations that naturally map onto SIMD units and massively parallel architectures. Building on this formulation, we provide optimized implementations spanning single- and multi-core CPUs, and, for the first time, GPU. Experimental evaluation demonstrates speedups of up to 14x on a single core, over 200x with multi-core, and over 570x on GPU over the state-of-the-art sequential baseline, establishing a new performance baseline for large-scale HSMM decoding.

Problem

• Hidden Semi-Markov Models
• Viterbi Algorithm
• GPU Acceleration
• Large-Scale Workloads

Innovation

• tensor-based formulation
• Viterbi algorithm
• HSMMs
• GPU acceleration
• parallel architectures