The alpaka library is a header-only C++14 abstraction library for accelerator development.
Its aim is to provide performance portability across accelerators through the abstraction (not hiding!) of the underlying levels of parallelism.
atoMEC is a python-based average-atom code for simulations of high energy density phenomena such as in warm dense matter. It is designed as an open-source and modular python package.
LLAMA is a cross-platform C++17 template header-only library for the abstraction of memory access patterns. It distinguishes between the view of the algorithm on the memory and the real layout in the background. This enables performance portability for multicore, manycore and gpu applications with the very same code.
MALA is a data-driven framework to generate surrogate models of density functional theory calculations based on machine learning. Its purpose is to enable multiscale modeling by bypassing computationally expensive steps in state-of-the-art density functional simulations.
The Python package minterpy is based on an optimised implementation of the multivariate interpolation algorithm given by M. Hecht et al. [1,2]. It thereby provides software solutions that lift the curse of dimensionality from interpolation tasks. While interpolation occurs as the bottleneck of most computational challenges, minterpy aims to free empirical sciences from their computational limitations.
openPMD is an open meta-data schema that provides meaning and self-description for data sets in science and engineering. The openPMD-api, developed openly in collaboration of the Center for Advanced Systems Understanding, Helmholtz-Zentrum Dresden-Rossendorf and Lawrence Berkeley National Laboratory, is an IO middleware library that assists domain-scientists with data description along the openPMD standard for FAIR particle-mesh data, used already in numerous physics simulations.