从CPU和GPU架构上的结构图像对三点相关函数的评估:考虑异构性效应
Vasily Postnicov1,2, Marina V Karsanina1,3, Aleksey Khlyupin1,3
1<a href="https://ror.org/00v0z9322">Moscow Institute of Physics and Technology</a>, Dolgoprudny, 141701, Russia.
Physical review. E
|November 20, 2024
概括
本研究介绍了计算三点相关函数 (CF) 的高效算法,比两点CF更深入地了解复杂结构. 开发的开源代码显著加快了各种应用程序的计算速度,包括流分析和深度学习.
科学领域:
- 物理 物理学 物理
- 计算科学 计算科学
- 数据分析 数据分析
背景情况:
- 经典的两点相关函数 (CF) 是分析结构的标准,但可能会错过非高斯签名.
- 三点CF提供了更丰富的信息内容,并可以捕获复杂的签名错过了两点方法.
研究的目的:
- 为计算各种三点相关函数 (S3,C3,Fsss,Fssv,Fsvv) 开发和实施高效的算法.
- 能够计算定向的三点CF,以进行增强的结构分析.
- 为可访问和可重复的研究提供一个开源包.
主要方法:
- 开发了用于计算直角和任意三角形模式的三点CF的算法.
- 实现预先计算的正则位置,以实现任意模式的高计算速度.
- 评估了图像和图案旋转对CF精度的影响,发现了轻微的影响.
主要成果:
- 与现有方法相比,在执行时间方面实现了显著的加快 (数量级).
- 证明了直角三角形模式在定向三点CF计算中的优势.
- 旋转对CF精度的影响较小,确保了强度.
结论:
- 开发的算法和开源软件包 (correlationfunctions.jl) 为分析复杂结构提供了强大的工具.
- 三点CF对于涉及非高斯数据,深度学习和结构分类的应用至关重要.
- 该代码可扩展到n点CF,为未来对更高阶统计的研究铺平了道路.
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