All software

AROSICS

AROSICS is an automated and robust open-source image co-registration software for multi-sensor satellite data.

GOLEM

GOLEM is a modelling platform for thermal-hydraulic-mechanical and non-reactive chemical processes in fractured and faulted porous media, based on the MOOSE framework

XRAYPAC

A software package for modeling x-ray-induced dynamics of matter

JEMRIS

JEMRIS is a versatile, open-source, multi-purpose MRI simulator. It is well-suited for research and education.

Materials Learning Algorithms

MALA (Materials Learning Algorithms) is a data-driven framework to accelerate electronic structure calculations based on machine learning. Its purpose is to construct neural-network surrogate models for bypassing computationally expensive steps in state-of-the-art density functional simulations.

PEPC

The PEPC project (Pretty Efficient Parallel Coulomb Solver) is a public tree code that has been developed at Jülich Supercomputing Centre since the early 2000s. Our code is a non-recursive version of the Barnes-Hut algorithm, using a level-by-level approach to both tree construction and traversals.

alpaka

The alpaka library is a header-only C++17 abstraction framework designed for computing accelerator development. It enables developers to implement algorithms once and execute them across a range of platforms, including x86, ARM, and RISC-V CPUs, as well as accelerators from NVIDIA, AMD, and Intel.

Allpix Squared

Allpix Squared is a semiconductor detector simulation framework. The goal of the framework is to provide a complete and easy-to-use package for end-to-end simulations of semiconductor detector performance from incident ionizing radiation until the digitization of hits in the detector chip.

Earth System Model Evaluation Tool (ESMValTool)

MPTRAC

Massive-Parallel Trajectory Calculations (MPTRAC) is a Lagrangian particle dispersion model for the analysis of atmospheric transport processes in the free troposphere and stratosphere.

KaGen - Communication-free Massively Distributed Graph Generators

Network generators help to provide synthethic instances with controllable parameters by algorithm developers and researchers. KaGen provides generators for a large variety of network models, which are communication-free by making use of pseudorandomization and divide-and-conquer schemes.

FatSegNet

A fully automated deep learning pipeline for adipose tissue (visceral and subcutaneous) segmentation on abdominal Dixon MRI.