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English(EN) High-Performance Tensor Formulation of the Viterbi Algorithm for Hidden Semi-Markov Models

新的维特比算法公式提高了 GPU 上 HSMM 解码性能

研究人员开发了一种用于隐半马尔可夫模型 (HSMM) 的维特比算法的新张量公式,显著提高了计算性能。这种新颖的方法重新构建了算法的操作,以利用 SIMD 单元和大规模并行架构,实现了首次 GPU 加速实现。实验表明,与现有的顺序方法相比,在单核 CPU 上速度提高了 14 倍,在多核 CPU 上提高了 200 多倍,在 GPU 上提高了 570 多倍。这一进步使得大规模 HSMM 解码在计算生物学和金融等各个领域都变得实用。 AI

影响 能够更高效、更可扩展地解码人工智能和机器学习应用中使用的复杂概率模型。

排序理由 详细介绍新颖算法公式及其性能评估的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的维特比算法公式提高了 GPU 上 HSMM 解码性能

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详细介绍新颖算法公式及其性能评估的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

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

    面向隐半马尔可夫模型的维特比算法高性能张量化表示

    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 …