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Principles towards Real-Time Simulation of Material Point Method on Modern GPUs

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arxiv 2111.00699 v1 pith:M55TCOJI submitted 2021-11-01 cs.GR cs.AR

classification cs.GRcs.AR
keywords real-timetimesgpusparticlespipelineprinciplesachievesapplications
verification ladder T0 review T1 audit T2 compute T3 formal

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abstract

Physics-based simulation has been actively employed in generating offline visual effects in the film and animation industry. However, the computations required for high-quality scenarios are generally immense, deterring its adoption in real-time applications, e.g., virtual production, avatar live-streaming, and cloud gaming. We summarize the principles that can accelerate the computation pipeline on single-GPU and multi-GPU platforms through extensive investigation and comprehension of modern GPU architecture. We further demonstrate the effectiveness of these principles by applying them to the material point method to build up our framework, which achieves $1.7\times$--$8.6\times$ speedup on a single GPU and $2.5\times$--$14.8\times$ on four GPUs compared to the state-of-the-art. Our pipeline is specifically designed for real-time applications (i.e., scenarios with small to medium particles) and achieves significant multi-GPU efficiency. We demonstrate our pipeline by simulating a snow scenario with 1.33M particles and a fountain scenario with 143K particles in real-time (on average, 68.5 and 55.9 frame-per-second, respectively) on four NVIDIA Tesla V100 GPUs interconnected with NVLinks.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CK-MPM: A Compact-Kernel Material Point Method

    cs.GR 2024-12 conditional novelty 7.0 of 10

    CK-MPM uses a C^2-continuous single-cell kernel on two offset grids to make MPM transfers cheaper, less diffusive, and stable.

  2. ControlHair: Synergizing Physics Simulator and Video Diffusion for Controllable Dynamic Hair Rendering

    cs.GR 2025-09 conditional novelty 6.0 of 10

    ControlHair couples Blender hair simulation with a video diffusion transformer to generate photorealistic videos with controlled hair dynamics from a single reference image.

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