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New Viterbi algorithm formulation boosts HSMM decoding performance on GPU

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new tensor-based formulation of the Viterbi algorithm for Hidden Semi-Markov Models (HSMMs), significantly improving computational performance. This novel approach restructures the algorithm's operations to leverage SIMD units and massively parallel architectures, enabling the first-ever GPU-accelerated implementations. Experiments show substantial speedups, with gains of up to 14x on single-core CPUs, over 200x on multi-core CPUs, and over 570x on GPUs compared to existing sequential methods. This advancement makes large-scale HSMM decoding practical across various domains, including computational biology and finance. AI

IMPACT Enables more efficient and scalable decoding for complex probabilistic models used in AI and machine learning applications.

RANK_REASON Academic paper detailing a novel algorithm formulation and its performance evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Viterbi algorithm formulation boosts HSMM decoding performance on GPU

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Academic paper detailing a novel algorithm formulation and its performance evaluation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Lorenzo Piarulli, Elia Belli, Daniele De Sensi ·

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

    arXiv:2609.16500v1 Announce Type: new 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 …

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