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English(EN) GPUSparse: GPU-Accelerated Learned Sparse Retrieval with Parallel Inverted Indices

GPUSparse系统利用GPU并行化加速学习稀疏检索

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了GPUSparse，一个旨在通过利用GPU并行化来加速学习稀疏检索模型的新系统。该系统解决了当前稀疏检索方法中存在的CPU瓶颈问题，该问题阻碍了实时性能。GPUSparse引入了GPU并行倒排索引、批处理的scatter-add评分算法以及融合的Triton内核，在保持高检索质量的同时实现了显著的加速。 AI

影响 这一发展有望实现大规模学习稀疏检索模型的实时服务，从而提高搜索和推荐系统的性能。

排序理由 该条目描述了一个新系统及其在学术论文中提出的性能评估。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.IR (Information Retrieval) 阅读 →

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

GPUSparse系统利用GPU并行化加速学习稀疏检索

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该条目描述了一个新系统及其在学术论文中提出的性能评估。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Ashutosh Sharma ·

    GPUSparse: GPU 加速的、基于学习的稀疏检索与并行倒排索引

    Learned sparse retrieval models such as SPLADE achieve retrieval quality competitive with dense models while preserving the interpretability and exact-match advantages of sparse representations. However, inference-time scoring still relies on CPU-bound inverted index traversal al…

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