Cell Lists is an algorithm that solves the fixed-radius near neighbors problem. That is, it finds all pairs of points that are within a fixed distance apart from each other. We can use the Cell Lists algorithm as a part of molecular dynamics or agent-based simulations where the interaction potential has a finite range.
You can read more about it in the article Searching for Fixed-Radius Near Neighbors with Cell Lists Algorithm in Julia Language, which explores the Cell Lists algorithm and theory behind it more deeply. We also extended the algorithm to a multithreaded version, which we explain in the article Multithreading in Julia Language in Julia Language Applied to Cell Lists Algorithm.
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repository and code by navigating to the DOI provided by Zenodo and then choosing your preferred citation format from the Export section. For example, we can export BibTex format. Alternatively, you can use the Cite This Repository button below the About section in the right sidebar.
You can install CellLists.jl
with the Julia package manager.
pkg> add CellLists
Alternatively, you can install CellLists.jl
directly from the GitHub repository.
pkg> add https://github.com/jaantollander/CellLists.jl
We can use CellLists.jl
by supplying n
, d
-dimensional points, and fixed radius r
to the CellList
constructor.
using CellLists: CellList, near_neighbors, distance_condition
n, d, r = 10, 2, 0.1
p = rand(n, d)
c = CellList(p, r)
By calling the near_neighbors
function, we obtain a list of index pairs of points that are within r
distance.
indices = near_neighbors(c, p, r)
[(3, 6), (4, 5), ...] # indices
We can compare Cell Lists to the brute force method.
indices2 = Vector{Tuple{Int, Int}}()
for i in 1:(n-1)
for j in (i+1):n
if distance_condition(p[i, :], p[j, :], r)
push!(indices2, (i, j))
end
end
end
The outputs should be equal as follows:
@assert Set(Set.(indices)) == Set(Set.(indices2))
On average, the Cell List algorithm is more efficient than brute force when dimensions d
is small, the number of points n
is sufficiently large, and radius r
is small compared to the bounding box of the points.
We can use the multithreaded version of Cell Lists by dispatching with the Val(:threads)
value type.
c = CellLists(p, r, Val(:threads))
near_neighbors(c, p, r, Val(:threads))
You can find the benchmarking code from the CellListsBenchmarks.jl repository and scripts for running the benchmarks and plotting in the cell-lists-benchmarks repository.