Visualization for Astrophysics & Cosmology

VisIVO

Responsible Partner: INAF

VisIVO (Visualization Interface for the Virtual Observatory) performs multi-dimensional data analysis and knowledge discovery of a-priori unknown relationships between multivariate and complex datasets in Astrophysics and Cosmology.

In particular, VisIVO Server offers high performance visualization on distributed and high performance platforms supporting particles and volume rendering of very large scale datasets.

In SPACE CoE we are further developing VisIVO to enhance the portability and reproducibility of the VisIVO modular applications for high performance visualization of data generated on the (pre-)exascale systems by the A&C simulations thanks to workflow systems that simplify the definition, execution, and management of the different visualization tasks. Our strategy consists of the integration of VisIVO with StreamFlow, which addresses the need to improve portability by decoupling multiple components from platform-specific dependencies, while also fostering reproducibility and maintainability. Moreover, the proposed solution takes advantage of a flexible resource use over heterogeneous HPC facilities.

Repo: github.com/VisIVOLab/VisIVOServer

Documentation: visivo.readthedocs.io/en/latest/

Video material: VisIVO for DEMNuni 2 | VisIVO for DeMNUni

VisIVO

Typical visualization pipeline in VisIVO (showing its three main modules) applied to GADGET cosmological simulation outputs.

VisIVO

Examples of VisIVO workflow outputs. Volume rendering of Cold Dark Matter particles of a large-scale GADGET cosmological simulation at redshift z = 0.

Hecuba

Responsible Partner: BSC-CNS

Hecuba is a set of tools and interfaces that implement a simple and efficient access to data stores for big data applications. Hecuba implements an Object Mapper for Cassandra, a recognized noSQL distributed database, that allows programmers to use a common interface to access data as regular in-memory objects, regardless if they are persistent (stored in disk) or they are actual in-memory data.

To provide a mechanism to synchronize run-time visualizations we are extending Hecuba to implement a lambda architecture integrating Apache Kafka. With this approach, Hecuba is allowed at the same time to persist the generated data in a key-value datastore and to produce a stream of data for on-line visualization. Thus, Hecuba is able to support both off-line and on-line data management.

In SPACE CoE we are focussed on completing the development of the Hecuba streaming feature to fulfill the requirements of the use case. For example, we have added to Hecuba the possibility to transparently partition large data, as generated by the simulations, to meet the limitations of the underlying streaming system. We also are going to implement a writer for our use case (Changa simulator) to use Hecuba to manage the output data.

Also, in this project we have developed a Paraview plugin with Hecuba integrated. Within this plugin, as normally, the user uses the ParaView application window displaying the visualization of data. The user will be able to request the data he wants to read from the stream, browse through the different timesteps of the simulation and visualize them in the view window.

Repo: github.com/bsc-dd/hecuba

Documentation: github.com/bsc-dd/hecuba/wiki/1:-User-Manual

Hecuba

Paraview application window with the plugin.

Volume-based cinematic visualisation in Blender

Responsible Partner: IT4Innovations, VSB - Technical University of Ostrava

Blender is a powerful open-source 3D software widely used for modeling, animation, and rendering. While originally designed for creative projects, it has become a valuable tool in scientific visualization, especially in astrophysics and cosmology. Its support for Python scripting, flexible interface, and compatibility with various data formats make it ideal for processing and visualizing large-scale simulation data.

To handle large datasets, a scalable visualization workflow is being developed. This workflow transforms simulation outputs into volumetric formats suitable for real-time volume rendering using Blender’s Cycles engine. The system allows users to interactively adjust lighting, shaders, and camera angles in real time, then render final high-quality images using offline path tracing. By integrating Blender with high-performance computing (HPC), the workflow offloads heavy processing to remote servers while maintaining user control locally.

In SPACE CoE we are further developing a new ecosystem focused on improving usability and performance, built around key tools like SpaceConverter and BSpace. SpaceConverter streamlines data preparation by converting simulation outputs into formats such as OpenVDB, ready for visualization. The BSpace add-on integrates directly with Blender, providing a user-friendly interface to load and render this data, making Blender a powerful and efficient platform for astrophysical visualization.

Repo: code.it4i.cz/blender/space_converter/

Documentation: code.it4i.cz/blender/space_converter/-/wikis/home

Blender

The workflow for processing and visualizing astrophysical and cosmological simulation data using the SpaceConverter ecosystem.