Scientific computing augmentation paving the way for the advent of next-generation synchrotron imaging research is discussed.

Python is one of the most popular programming languages for scientific computing. This article shows how to achieve high performance, using specialized libraries and parallel computing, to solve crystallographic problems.

The integration of Capsules within the SBGrid software-management platform marks a pivotal advancement in addressing the challenges of scientific software distribution, dependency management and computational reproducibility.

An introduction to the current paradigm shift towards concurrency in software.

A package for Mathematica has been developed, containing the most important point- and space-group information together with tables for various photon–atom interactions. It includes basic functions for handling crystallographic data as well as procedures for calculating various quantities in relation to crystallography and X-ray diffraction.

An application of formal verification by theorem proving in Isabelle/HOL to ensure the correctness of a space-group-derivation algorithm is presented.

The amount of data generated during crystallographic fragment-screening projects requires sophisticated automated methods to analyse the data and to identify binders. FragMAXapp is the MAX IV Laboratory approach to managing fragment-screening campaigns: a web application that provides scientists with access to the MAX IV computing cluster and visualization tools.

A technical description of the Dynamo software package for subtomogram averaging is provided. Details are given on advanced MATLAB libraries, parallelization strategies, the use of GPUs and accessibility though the Amazon cloud computing services.

The recently inaugurated beamline ID10-BEATS for hard X-ray full-field tomography at the SESAME synchrotron in Jordan is presented. The design, performance and scientific applications of the beamline, which was developed within the European Horizon 2020 project BEAmline for Tomography at SESAME, are illustrated.

PyNX is a toolkit with assorted Python modules and command-line scripts which can be used for the analysis and simulation of coherent X-ray imaging, including techniques such as coherent diffraction imaging, ptychography and wavefront propagation, in the near- or far-field regime. All calculations can be executed solely on graphical processing units (GPUs) for accelerated computing. Elementary algorithms can be easily tailored, built upon and combined using an operator-based approach, allowing full flexibility with high-performance computing. Calculations can be distributed on multiple GPUs using MPI, e.g. for large ptychography data sets.