High level GPU-accelerated 2D PIV framework in Python

The Particle Image Velocimetry (PIV) method is widely used for optical measurment of flow velocity fields. This paper demonstrates the possibilities of using high-level libraries for GPU-accelerated PIV data analysis in Python. The Torch PIV library for the analysis of 2D PIV experiments based on the deep learning framework PyTorch with CUDA support was developed. The library implements a multi pass cross-correlation FFT PIV algorithm with an interrogation window shift. The chosen implementation does not require compilation from the user, has a compact codebase, is able to run both on the CPU and the GPU depending on the user choice, and also it is as flexible as the Python module. In this work, the performance of the CPU version of the developed method was compared with existing open source implementations. It is shown that the main functions of the developed module can be executed on the GPU at the speed of CUDA implementations. The developed library is tested on synthetic images and experimental data.

粒子图像测速法（Particle Image Velocimetry, PIV）是一种被广泛应用于流场速度光学测量的技术。本文论证了在Python环境中使用高级库开展GPU加速的PIV数据分析的可行性。研究开发了一款基于深度学习框架PyTorch并支持CUDA的二维PIV实验分析库Torch PIV。该库实现了带有查询窗口偏移的多遍互相关快速傅里叶变换（FFT）PIV算法。所采用的实现方案无需用户手动编译，代码库简洁紧凑，可根据用户选择在中央处理器（CPU）与图形处理器（GPU）上运行，同时具备Python模块般的灵活性。本研究将所开发方法的CPU版本性能与现有开源实现进行了对比，结果表明，所开发模块的核心功能可在GPU上达到CUDA实现的运行速度。所开发的库已在合成图像与实验数据上完成测试。