PointNeighbors.jl is a package for neighborhood search with fixed search radius in 1D, 2D and 3D point clouds.
- Several implementations of neighborhood search with fixed search radius
- Focus on fast incremental updates to be usable for particle-based simulations with frequent updates
- Designed as a "playground" to easily switch between different implementations and data structures
- Common API over all implementations
- Extensive benchmark suite to study different implementations (work in progress)
- GPU compatibility (work in progress)
| Implementation | Description | Features | Query | Update | GPU-compatible |
|---|---|---|---|---|---|
GridNeighborhoodSearch with DictionaryCellList |
Grid-based NHS with Julia Dict backend |
Infinite domain | Fast | Fast | ❌ |
GridNeighborhoodSearch with FullGridCellList |
Grid-based NHS allocating all cells of the domain | Finite domain, but efficient memory layout for densely filled domain. | Faster | Fastest | ✅ |
PrecomputedNeighborhoodSearch |
Precompute neighbor lists | Best for TLSPH without NHS updates. Not suitable for updates in every time step. | Fastest | Very slow | ✅ |
The following benchmarks were conducted on an AMD Ryzen Threadripper 3990X using 128 threads.
Benchmark of a single force computation step of a Weakly Compressible SPH (WCSPH) simulation:
Benchmark of an incremental update similar to a WCSPH simulation (note the log scale):
Benchmark of a full right-hand side evaluation of a WCSPH simulation (note the log scale):
Benchmark of a single force computation step of a Weakly Compressible SPH (WCSPH) simulation
with the GridNeighborhoodSearch and FullGridCellList implementation:
Benchmark of a single force computation step of a Total Lagrangian SPH (TLSPH) simulation
with the PrecomputedNeighborhoodSearch implementation (TLSPH is looking for neighbors
in the initial configuration, so the NHS does not need to be updated):
Deformation gradient performance for a TLSPH simulation with
the PrecomputedNeighborhoodSearch implementation:
If you're using PointNeighbors.jl in your package, please feel free to open a PR adding it to this list.
If you use PointNeighbors.jl in your own research or write a paper using results obtained with the help of PointNeighbors.jl, please cite it as
@misc{pointneighbors,
title={{P}oint{N}eighbors.jl: {N}eighborhood search with fixed search radius in {J}ulia},
author={Erik Faulhaber and Niklas Neher and Sven Berger and
Michael Schlottke-Lakemper and Gregor Gassner},
year={2024},
howpublished={\url{https://github.com/trixi-framework/PointNeighbors.jl}},
doi={10.5281/zenodo.12702157}
}