Simulating fluids using a coupled voxel-particle data model

Bailey, Dan; Warner, Matt; Biddle, Harry

doi:10.1145/2504459.2504478

Cited by 2 publications

(2 citation statements)

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“…Nielsen and Bridson [2016] propose a wide-branching tile tree of voxels for fluid simulation. Bailey et al [2013] sort particles to corresponding voxel blocks, similar to our "Hierarchical Particle Buckets" described in Section 6.1. Outside of simulation, Kazhdan et al [2006] and Agarwala [2007] used octrees for solving Poisson's equation for image stitching and mesh reconstruction, respectively.…”

Section: Hierarchical Sparse Grids In Graphicsmentioning

confidence: 99%

Taichi

Hu¹,

Anderson³

et al. 2019

ACM Trans. Graph.

149

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3D visual computing data are often spatially sparse. To exploit such sparsity, people have developed hierarchical sparse data structures, such as multi-level sparse voxel grids, particles, and 3D hash tables. However, developing and using these high-performance sparse data structures is challenging, due to their intrinsic complexity and overhead. We propose Taichi , a new data-oriented programming language for efficiently authoring, accessing, and maintaining such data structures. The language offers a high-level, data structure-agnostic interface for writing computation code. The user independently specifies the data structure. We provide several elementary components with different sparsity properties that can be arbitrarily composed to create a wide range of multi-level sparse data structures. This decoupling of data structures from computation makes it easy to experiment with different data structures without changing computation code, and allows users to write computation as if they are working with a dense array. Our compiler then uses the semantics of the data structure and index analysis to automatically optimize for locality, remove redundant operations for coherent accesses, maintain sparsity and memory allocations, and generate efficient parallel and vectorized instructions for CPUs and GPUs. Our approach yields competitive performance on common computational kernels such as stencil applications, neighbor lookups, and particle scattering. We demonstrate our language by implementing simulation, rendering, and vision tasks including a material point method simulation, finite element analysis, a multigrid Poisson solver for pressure projection, volumetric path tracing, and 3D convolution on sparse grids. Our computation-data structure decoupling allows us to quickly experiment with different data arrangements, and to develop high-performance data structures tailored for specific computational tasks. With 1 <u> 1 </u> 0 th as many lines of code, we achieve 4.55× higher performance on average, compared to hand-optimized reference implementations.

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Section: Hierarchical Sparse Grids In Graphicsmentioning

confidence: 99%

Taichi

Hu¹,

Anderson³

et al. 2019

ACM Trans. Graph.

149

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show abstract

“…Our tightly-coupled, voxel-particle data storage [Bailey 2013], uses the voxel index to order the particle data. This technique can be leveraged to pack and unpack the positional data into smaller storage that reduces memory usage, improves I/O bandwidth and occupies less disk space.…”

Section: Introductionmentioning

confidence: 99%

Packing the water pipe

Bailey

Warner

Biddle

2014

ACM SIGGRAPH 2014 Talks

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From huge tidal waves to complex character interactions, FLIP fluid simulations based on volumes and particles have become a key aspect of a modern water pipeline. OpenVDB has recently introduced efficient sparse volumes to production to reduce storage and memory requirements for volumes. We build on this goal with new lossless and lossy particle compression schemes designed to significantly reduce the memory, storage and I/O bandwidth associated with processing large particle sets for simulation and rendering.

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Simulating fluids using a coupled voxel-particle data model

Cited by 2 publications

References 0 publications

Taichi

Taichi

Packing the water pipe

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