The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Poster Type: Research Posters
Author: Mengjiao Han (Argonne National Laboratory (ANL)), Andres Sewell (Utah State University), Joseph Insley (Argonne National Laboratory (ANL)), Janet Knowles (Argonne National Laboratory (ANL)), Victor A. Mateevitsi (Argonne National Laboratory (ANL), University of Illinois Chicago), Michael E. Papka (Argonne National Laboratory (ANL), University of Illinois Chicago), Steve Petruzza (Utah State University), Silvio Rizzi (Argonne National Laboratory (ANL))
Supervisor:
Abstract: 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.
Best Poster Finalist (BP): no
Poster: PDF
Poster Summary: PDF