Distributing liquids using OpenVDB

Figure 1: Two simulations with a (background-)grid resolution of 200 3 : Our method (left) is nearly indistinguishable from regular FLIP (right), but uses only one million particles instead of 24 million. Computations outside of the pressure solver are sped up by a factor of more than 6, the total computation time for 250 frames is reduced from 4.4h to 2h, and the average memory footprint is reduced by a factor of 2. AbstractThe Fluid Implicit Particle method (FLIP) for liquid simulations uses particles to reduce numerical dissipation and provide important visual cues for events like complex splashes and small-scale features near the liquid surface. Unfortunately, FLIP simulations can be computationally expensive, because they require a dense sampling of particles to fill the entire liquid volume. Furthermore, the vast majority of these FLIP particles contribute nothing to the fluid's visual appearance, especially for larger volumes of liquid. We present a method that only uses FLIP particles within a narrow band of the liquid surface, while efficiently representing the remaining inner volume on a regular grid. We show that a naïve realization of this idea introduces unstable and uncontrollable energy fluctuations, and we propose a novel coupling scheme between FLIP particles and regular grid which overcomes this problem. Our method drastically reduces the particle count and simulation times while yielding results that are nearly indistinguishable from regular FLIP simulations. Our approach is easy to integrate into any existing FLIP implementation.

Section: Introductionmentioning

confidence: 99%

Narrow Band FLIP for Liquid Simulations

Ferstl

Ando

Wojtan

et al. 2016

“…Fluid simulations can leverage distributed and heterogeneous platforms, by distributing sparse data structures on clusters [BBAW15] and GPUs [WTYH18], distributing solvers [LMAS16] on GPUs and distributing material point methods on GPUs [GWW*18].…”

Section: Related Workmentioning

confidence: 99%

Accelerating Distributed Graphical Fluid Simulations with Micro‐partitioning

Mashayekhi

Shah

et al. 2019

Graphical fluid simulations are CPU‐bound. Parallelizing simulations on hundreds of cores in the computing cloud would make them faster, but requires evenly balancing load across nodes. Good load balancing depends on manual decisions from experts, which are time‐consuming and error prone, or dynamic approaches that estimate and react to future load, which are non‐deterministic and hard to debug. This paper proposes Birdshot scheduling, an automatic and purely static load balancing algorithm whose performance is close to expert decisions and reactive algorithms without their difficulty or complexity. Birdshot scheduling's key insight is to leverage the high‐latency, high‐throughput, full bisection bandwidth of cloud computing nodes. Birdshot scheduling splits the simulation domain into many micro‐partitions and statically assigns them to nodes randomly. Analytical results show that randomly assigned micro‐partitions balance load with high probability. The high‐throughput network easily handles the increased data transfers from micro‐partitions, and full bisection bandwidth allows random placement with no performance penalty. Overlapping the communications and computations of different micro‐partitions masks latency. Experiments with particle‐level set, SPH, FLIP and explicit Eulerian methods show that Birdshot scheduling speeds up simulations by a factor of 2‐3, and can out‐perform reactive scheduling algorithms. Birdshot scheduling performs within 21% of state‐of‐the‐art dynamic methods that require running a second, parallel simulation. Unlike speculative algorithms, Birdshot scheduling is purely static: it requires no controller, runtime data collection, partition migration or support for these operations from the programmer.

“…[ABO16] introduce a topologically correct, boundary‐conforming cut‐cell mesh to simulate liquid on very coarse grids, while Liu et al [LMAS16] and Chu et al [CZY17] present a Schur‐complement for solving the Poisson equation on a decomposed domain in parallel. Bailey et al [BBAW15] also employ sparse volumetric structures on distributed systems for large liquid simulations.…”

Section: Introductionmentioning

confidence: 99%

Fast Fluid Simulations with Sparse Volumes on the GPU

Truong

Yuksel

et al. 2018

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.