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English(EN) TileMaxSim: IO-Aware GPU MaxSim Scoring with Dimension Tiling and Fused Product Quantization

TileMaxSim内核将GPU检索模型速度提升220倍

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了TileMaxSim，这是一种新的面向IO的GPU内核，旨在显著加速多向量检索模型（如ColBERT）中使用的MaxSim评分过程。现有实现效率低下，仅利用了可用GPU带宽的一小部分。TileMaxSim通过采用多查询SRAM分块、维度分块和融合乘积量化评分来解决此问题，在NVIDIA H100 GPU上实现了高达80.2%的峰值HBM带宽。这带来了显著的速度提升，能够实现每秒对8200万份文档进行评分，并大大降低了检索任务的延迟。 AI

影响 显著加速检索模型性能，可能实现更快、更高效的AI驱动的搜索和推荐系统。

排序理由 该条目是一篇研究论文，详细介绍了一种用于提高信息检索中GPU性能的新技术方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.IR (Information Retrieval) 阅读 →

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TileMaxSim内核将GPU检索模型速度提升220倍

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该条目是一篇研究论文，详细介绍了一种用于提高信息检索中GPU性能的新技术方法。[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 ·

    TileMaxSim：具有维度分块和融合乘积量化的面向IO的GPU MaxSim评分

    Multi-vector retrieval models such as ColBERT achieve state-of-the-art accuracy through fine-grained token-level MaxSim scoring, yet existing GPU implementations leave most hardware performance unused. We give a roofline analysis of MaxSim on modern GPUs and identify a severe ban…

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