Paper Info
Title
NGrid: a proximity data structure for fluids animation with GPU computing
Abstract
This paper introduces a novel and efficient data structure capable of supporting a large number of particle-based elements in a GPU architecture such as fluid's animation. The presented fluid animation approach is based on SPH (Smoothed Particle Hydrodynamics) and uses a unique algorithm for the neighborhood gathering, required during a particle processing. Usually, this kind of information about neighborhood is provided by algorithm which use spatial data structures, subdividing the environment and classifying each particle among their position in space. Unfortunately, it does not provide efficiency for a large number of particles grouped closes to each other as most of them will fall in the same cell. Instead of using such approaches, this work presents a novel and efficient data structure that maintains the particles into another kind of proximity data structure, called NGrid. In this structure, each cell contains only one particle and does not directly represent a discrete spatial subdivision. The NGrid does process an approximate spatial neighborhood of the particles, yielding promising results for real time fluid animation, with results that goes up to 8× speedup, when compared to traditional GPU approaches, and up to 100× when compared against CPU implementations.
Year
DOI
Venue
2015
10.1145/2695664.2695827
Selected Areas in Cryptography
Keywords
Field
DocType
Fluid Animation, Gpu Computing, Data Structure, CUDA
Spatial analysis,Smoothed-particle hydrodynamics,Data structure,Computer graphics (images),CUDA,Computer science,Computational science,Subdivision,Animation,General-purpose computing on graphics processing units,Speedup
Conference
Citations 
PageRank 
References 
0
0.34
14
Authors
3
Name
Order
Citations
PageRank
Mark Joselli100.34
José Ricardo da S. Junior200.34
Esteban W. Gonzalez Clua327953.69