Speaker
Description
Modern cosmological simulations produce large and complex data sets, often containing tens of billions of particles spread across terabytes of files. Although these simulations are essential for deepening our understanding of galaxy formation, large-scale structures, and cosmic evolution, their scientific impact is limited without effective methods of visual analysis and communication. Bridging the gap between raw numerical outputs and high-quality film visualizations remains a significant challenge, especially when working in high-performance computing (HPC) environments.
In this talk, we will present an integrated workflow and toolkit designed to make cosmological data more accessible, interactive, and visually appealing. Our workflow focuses on three new components: BSpace, a Blender add-on that provides a user-friendly interface for interacting with astrophysical datasets; CyclesPhi, a modified version of the Blender Cycles renderer optimized for HPC clusters; and SpaceConverter, a standalone software application for converting particle-based outputs from simulation codes such as HACC, ChaNGa, and OpenGADGET into volume formats suitable for high-quality rendering.
The BSpace add-on allows users to connect Blender directly to local or remote HPC resources, select regions of interest, configure parameters, and import volumetric data files into Blender scenes for inspection and animation. Renderer CyclesPhi extends the Cycles engine, enabling distributed path tracing across multiple GPUs, and integrates seamlessly with BSpace for interactive remote rendering. The SpaceConverter application forms the backbone of the workflow, offering efficient parallel voxelization and conversion of SPH and N-body simulation data into OpenVDB or NanoVDB volumes. Its design supports both offline command-line processing and interactive integration with Blender, allowing researchers to balance automation, flexibility, and performance.
We demonstrate the ability to generate visually striking renderings of gas flows, dark matter structures, star distributions, and galactic magnetic fields, emphasizing both scientific detail and cinematic clarity. By combining robust HPC data processing with artist-friendly visualization tools, this framework lowers the barriers for astrophysicists to create publication-quality images and animations, while opening avenues for broader public engagement in cosmological research.
