PyNX: high performance computing toolkit for coherent X-ray imaging based on operators

TL;DR

PyNX is an open-source GPU-accelerated toolkit that simplifies coherent X-ray imaging data analysis and simulation, enabling high-performance computations for various imaging techniques with flexible, operator-based workflows.

Contribution

The toolkit has been extended to include GPU-accelerated tools for coherent X-ray imaging, with operator-based workflows and multi-GPU support for large datasets.

Findings

Supports high-speed computations on GPUs

Enables flexible operator-based imaging workflows

Facilitates large dataset processing with multiple GPUs

Abstract

The open-source PyNX toolkit [Favre-Nicolin et al (2011) arXiv:1010.2641, Mandula et al (2016)] has been extended to provide tools for coherent X-ray imaging data analysis and simulation. All calculations can be executed on graphical processing units (GPU) to achieve high performance computing speeds. This can be used for Coherent Diffraction Imaging (CDI), Ptychography and wavefront propagation, in the far or near field regime. Moreover, all imaging operations (propagation, projections, algorithm cycles..) can be used in Python as simple mathematical operators, an approach which can be used to easily combine basic algorithms in a tailored chain. Calculations can also be distributed to multiple GPUs, e.g. for large Ptychography datasets. Command-line scripts are also available for on-line CDI and Ptychography analysis, either from raw beamline datasets or using the Coherent X-ray Imaging data format [Maia (2012)].