GigaVoxels DP: Starvation-Less Render and Production for Large and Detailed Volumetric Worlds Walkthrough

Author:

Richermoz Antoine1ORCID,Neyret Fabrice1ORCID

Affiliation:

1. INRIA / LJK (CNRS, UGA, INP-G), Grenoble, France

Abstract

Using voxel hierarchies as a generic 3D scene representation makes ray marching, antialiasing, and LOD easy. The drawback is the huge amount of memory required to store voxels, even with empty space compression. Still, GigaVoxels [Crassin et al. 2009] showed that by using a ray-guided cache to produce and store only visible voxels bricks on demand, it is possible to walk through very large and detailed worlds with real-time performance in bounded GPU memory. However, on-demand production of data during rendering is still challenging in terms of synchronization and starvation of GPU cores. We propose a new GPU-driven algorithm using dynamic parallelism (DP) to minimize these, and a "GPU-cores timeline" profiling tool to analyze them. We validate our model with timings (2× gain) and we illustrate it on various scenes.

Publisher

Association for Computing Machinery (ACM)

Reference22 articles.

1. Andy Adinets. 2014. Adaptive Parallel Computation with CUDA Dynamic Parallelism. https://developer.nvidia.com/blog/introduction-cuda-dynamic-parallelism/

2. AMD. 2017. Radeon GPU Profiler. https://gpuopen.com/rgp

3. State-of-the-Art in GPU-Based Large-Scale Volume Visualization

4. Graham Wihlidal Brian Karis, Rune Stubbe. 2021. Nanite - A Deep Dive - SIGGRAPH 2021. https://advances.realtimerendering.com/s2021/Karis_Nanite_SIGGRAPH_Advances_2021_final.pdf

5. Interactively Modifying Compressed Sparse Voxel Representations

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