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高性能GPU实现KNN算法:一个审查
Pooja Bidye1, Pradnya Borkar1, Nitin Rakesh1
1Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, 412115, India.
MethodsX
|October 6, 2025
概括
在图形处理单元 (GPU) 上使用高性能计算 (HPC) 优化K-Nearest Neighbor (KNN) 算法可显著加快复杂,高维数据集的处理. 诸如内存访问优化和数据细分等技术可以实现显著的加快速度,提高机器学习性能.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 高性能计算 高性能计算
背景情况:
- 机器学习 (ML) 算法在标准CPU上与大型,复杂的数据集作斗争.
- 虽然K-Nearest Neighbor (KNN) 算法被广泛使用,但它面临着高维数据的性能挑战.
研究的目的:
- 在GPU平台上审查加速KNN算法的优化技术.
- 评估这些优化对HPC环境中的高维数据集的影响.
主要方法:
- 对GPU的KNN算法并行化和优化研究的审查.
- 分析技术,包括融合内存访问,块,块化,数据细分和基于枢纽的分区.
主要成果:
- 优化的KNN算法利用GPU能力实现了显著的加快速度.
- 在双GPU平台上,加速度达到750倍,在多GPU平台上达到1840倍,用于高维数据.
结论:
- 基于GPU的优化技术对于在大型高维数据集上加速KNN至关重要.
- 这项研究为HPC和ML应用中的研究人员提供了宝贵的见解.
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