3D Gaussian Splatting (3D-GS) has recently emerged as a powerful technique for real-time, photorealistic rendering by optimizing anisotropic Gaussian primitives from view-dependent images. While 3D-GS has been extended to scientific visualization, prior work remains limited to single-GPU settings, restricting scalability for large datasets on high-performance computing (HPC) systems. We present a distributed 3D-GS pipeline tailored for HPC. Our approach partitions data across nodes, trains Gaussian splats in parallel using multi-nodes and multi-GPUs, and merges splats for global rendering. To eliminate artifacts, we add ghost cells at partition boundaries and apply background masks to remove irrelevant pixels. Benchmarks on the Richtmyer-Meshkov datasets (about 106.7M Gaussians) show up to 3X speedup across 8 nodes on Polaris while preserving image quality. These results demonstrate that distributed 3D-GS enables scalable visualization of large-scale scientific data and provide a foundation for future in situ applications.
@article{arxiv.2509.12138,
title = {Distributed 3D Gaussian Splatting for High-Resolution Isosurface Visualization},
author = {Mengjiao Han and Andres Sewell and Joseph Insley and Janet Knowles and Victor A. Mateevitsi and Michael E. Papka and Steve Petruzza and Silvio Rizzi},
journal= {arXiv preprint arXiv:2509.12138},
year = {2025}
}