The community Computational Methods for Petascale Research was created from the eponomous workshop at the 26^th International Conference on Computational Science, which was held at DESY, Hamburg in Germany from 29 June to 1 July 2026.

This workshop gathered researchers and scientists who are working with experiments that acquire petabytes of data and/or require petaflops of computing power to simulate or to analyse their outputs. Such experiments and observatories require sophisticated and cutting edge data management solutions and drive research into reliable and scalable methods, frequently unique to science. Data acquisition, from triggering to streaming, and including fast data reduction and compression, is key to selecting science signals from noise. Efficient algorithms, and the use of modern processors such as GPUs and FPGAs, are essential to handle these data volumes and extract knowledge. Machine Learning techniques have become key in this field, and power ever more effective data processing, fast simulations and digital twin approaches.

The workshop solicited papers on all of the themes mentioned above and particularly highlights the topics of:

Data management at PB scale and beyond
Data streaming for high data rate experiments
Efficient algorithms for handling massive datasets on modern hardware
Machine learning applied to big data sciences
Digital twins and high performance simulations

This Zenodo community gathers materials from the workshop to give authors citable references to their presentations and to enhance the longevity and impact of the presentations.