Out-of-Core Proximity Computation for Particle-based Fluid Simulations
1 KAIST (Korea Advanced Institute of Science and Technology)
2 Ewha Womans University, Seoul, Korea
3 Dongguk University, Seoul, Korea
High Performance Graphics 2014
Video(45MB)
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These figures show a particle-based fluid simulation frame
of our two sources benchmark consisting of up to 65.6 M particles.
The right image zooms in simulated particles within a box shown in the left image.
Our epsilon-NN method takes 3.6 s on average per frame by using two hexa-core CPUs
and two Geforce GTX 780.
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Abstract
To meet the demand of higher realism, a high number of particles are used for
particle-based fluid simulations, resulting in various out-of-core issues. In
this paper, we present an out-of-core proximity computation, especially,
epsilon-Nearest Neighbor (epsilon-NN) search, commonly used for
particle-based fluid simulations, to handle such big data sets consisting of
tens of millions of particles.
Specifically, we identify a maximal work set that a GPU can process efficiently in an in-core mode.
As a main technical component, we compute a memory footprint for processing a given work set
based on our expectation model of the number of neighbors of particles.
Our method can naturally utilize heterogeneous computing resources
such as CPUs and GPUs, and has been applied to large-scale fluid simulations
based on smoothed particle hydrodynamics.
We have demonstrated that our method handles up to 65~M particles and processes up to 15~M
epsilon-NN queries per second by using two CPUs and a GPU, which has only 3~GB video memory.
This result is up to 51 times higher performance than a single CPU-core
version for the out-of-core case.
This high performance for large-scale data given a limited video memory space
is achieved mainly thanks to the high accuracy of our memory estimation method.
Contents
Paper(Author preprint): Out-of-Core Proximity Computation for Particle-based Fluid Simulations
HPG2014 Talk slides (45MB)
GTC2015 Talk recording
GTC 2015 Poster
Fluid Benchmarks (KAIST Model Benchmarks)
The code will be available soon!!
Technical Report: Dept. of CS, KAIST, Technical Report CS-TR-2014-385
Dept. of Computer Science
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