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UID:submissions.supercomputing.org_SC25_sess620_post225@linklings.com
SUMMARY:Distributed 3D Gaussian Splatting for High-Resolution Isosurface V
 isualization
DESCRIPTION:Mengjiao Han (Argonne National Laboratory (ANL)); Andres Sewel
 l (Utah State University); Joseph Insley and Janet Knowles (Argonne Nation
 al Laboratory (ANL)); Victor A. Mateevitsi and Michael E. Papka (Argonne N
 ational Laboratory (ANL), University of Illinois Chicago); Steve Petruzza 
 (Utah State University); and Silvio Rizzi (Argonne National Laboratory (AN
 L))\n\n3D Gaussian Splatting (3D-GS) has recently emerged as a powerful te
 chnique for real-time, photorealistic rendering by optimizing anisotropic 
 Gaussian primitives from view-dependent images. While 3D-GS has been exten
 ded to scientific visualization, prior work remains limited to single-GPU 
 settings, restricting scalability for large datasets on high performance c
 omputing (HPC) systems. We present a distributed 3D-GS pipeline tailored f
 or 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 bounda
 ries and apply background masks to remove irrelevant pixels. Benchmarks on
  the Richtmyer–Meshkov datasets (about 106.7M Gaussians) show up to 3X spe
 edup across 8 nodes on Polaris while preserving image quality. These resul
 ts demonstrate that distributed 3D-GS enables scalable visualization of la
 rge-scale scientific data and provide a foundation for future in situ appl
 ications.\n\nTag: Research & ACM SRC Posters\n\nRegistration Category: Tec
 hnical Program Reg Pass\n\nSession Chairs: Kento Sato (RIKEN Center for Co
 mputational Science (R-CCS)); Anja Gerbes (Georg-August-Universität Göttin
 gen); and Chris Schlipalius (Pawsey Supercomputing Research Centre; Common
 wealth Scientific and Industrial Research Organisation (CSIRO), Australia)
 \n\n
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