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Fast Fluid Simulations with Sparse Volumes on the GPU
Author(s) -
Wu Kui,
Truong Nghia,
Yuksel Cem,
Hoetzlein Rama
Publication year - 2018
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13350
Subject(s) - computer science , computational science , solver , parallel computing , grid , sparse matrix , conjugate gradient method , voxel , general purpose computing on graphics processing units , cuda , hierarchy , algorithm , computer graphics (images) , graphics , artificial intelligence , mathematics , physics , geometry , quantum mechanics , economics , market economy , gaussian , programming language
We introduce efficient, large scale fluid simulation on GPU hardware using the fluid‐implicit particle (FLIP) method over a sparse hierarchy of grids represented in NVIDIA ® GVDB Voxels. Our approach handles tens of millions of particles within a virtually unbounded simulation domain. We describe novel techniques for parallel sparse grid hierarchy construction and fast incremental updates on the GPU for moving particles. In addition, our FLIP technique introduces sparse, work efficient parallel data gathering from particle to voxel, and a matrix‐free GPU‐based conjugate gradient solver optimized for sparse grids. Our results show that our method can achieve up to an order of magnitude faster simulations on the GPU as compared to FLIP simulations running on the CPU.