High level GPU-accelerated 2D PIV framework in Python

Abstract

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. Program SummaryProgram title: TorchPIV.CPC Library link to program files: https://doi.org/10.17632/yw43vjc36h.1.Developer's repository link: https://github.com/NikNazarov/TorchPIV.Licensing provisions: MIT license.Programming language: Python.Nature of problem: PIV experiments often require analyzing a large number of images in order to determine the statistical characteristics of the flow. GPUs are actively used in this field to speed up the data analysis process. Open-source software solutions to this problem usually require build and are difficult to integrate into the analysis pipelines.Solution method: The main feature of the developed module is its flexibility and simple distribution. The module is cross-platform, and installation does not require compilation from the user. The developed library can be imported as an ordinary Python module, at the same time it allows to get a significant performance gain when analyzing PIV experiments by using NVIDIA GPUs. Implementation in pure Python allows the module to serve as a backend for more complex experimental data processing systems. The core library of this method is one of the most reliable and widespread in the field of machine learning.