Scientific Computing Core

Some research groups at CASUS employ condensed matter simulations. For that purpose, CASUS hosts a development group for CP2K. CP2K is a quantum-chemistry and solid-state physics software package capable of performing large-scale massively parallel calculations on different levels of theory.

The Alpaka library is a performance-portability programming model designed for various computing architectures, making it easier to develop and optimize C++ applications that run on different hardware. It abstracts the hardware specifics, so developers can write code that is portable across various platforms such as CPUs, GPUs, and other accelerators. Alpaka provides a uniform and flexible interface that helps in managing the complexities associated with different architectures, focusing on parallel computing paradigms. This allows developers to maximize performance without having to tailor their code for specific hardware platforms, enhancing productivity and scalability across diverse computational environments.

During the COVID19 pandemic CASUS developed and ran the Where2Test platform. It combined data collection about COVID infections with demographics as well as prediction and simulation of hypothetical scenarios. For this purpose it employed High-Performance Computing (HPC), epidemiological modelling, control theory, and numeric optimization. In addition to traditional scientific papers, the CASUS effort has produced several web applications that made the HPC forecasts available to general public, and provided useful tools for dealing with the practical aspects of life under pandemic such as workplace risk calculator, optimizer for office occupancy, or testing strategy optimizer for medical care facilities.

CASUS is a partner in several data driven, public–private partnership research projects. Among them are three notable digital health projects: PIONEER (big-data platform for prostate cancer patient data in Europe), OPTIMA (first interoperable and GDPR-compliant European real-world oncology data and evidence generation platform based on the needs of clinicians and patients with prostate, lung and breast cancer) and the UroEvidenceHub, an analytics platform for urology and related medical disciplines.

The CASUS Scientific Computing Core aims to facilitate collaboration between the CASUS research teams. Currently, we are involved in pairing the award-winning the Materials Learning Algorithms (MALA) software package with the multivariate polynomial interpolation package minterpy.

Scenery is a scenegraphing and rendering library. It allows you to quickly create high-quality 3D visualizations based on mesh data. Sciview is an ImageJ/FIJI plugin for 3D visualization of images and meshes and uses Scenery as a rendering back-end. Both Scenery and Sciview support rendering to head-mounted virtual reality (VR) goggles.

Research data management (RDM) is both a support activity of the SCC and its own research and development field. It is about defined data life cycles, organization of research data in teams, data sharing and data publications. The CASUS RDM is governed by the FAIR principles.

SciJava is an overview of available Java libraries for scientific computing. The packages listed are provided by individual projects each of which is committed to co-operating with the other projects, reusing libraries and integrating clients for a seamless workflow. The focus at the moment is geared towards biosciences.