We present a multi-GPU extension of the 3D Gaussian Splatting (3D-GS) pipeline for scientific visualization. Building on previous work that demonstrated high-fidelity isosurface reconstruction using Gaussian primitives, we incorporate a multi-GPU training backend adapted from Grendel-GS to enable scalable processing of large datasets. By distributing optimization across GPUs, our method improves training throughput and supports high-resolution reconstructions that exceed single-GPU capacity. In our experiments, the system achieves a 5.6X speedup on the Kingsnake dataset (4M Gaussians) using four GPUs compared to a single-GPU baseline, and successfully trains the Miranda dataset (18M Gaussians) that is an infeasible task on a single A100 GPU. This work lays the groundwork for integrating 3D-GS into HPC-based scientific workflows, enabling real-time post hoc and in situ visualization of complex simulations.
@article{arxiv.2509.05216,
title = {Toward 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:2509.05216},
year = {2025}
}