Comments (4)
On hold until Base threading is better.
from nearestneighbors.jl.
Thanks for this nice package. I was looking for some parallel versions for knn
and it led me here. Could you please throw some light on what is the plan and how could any of us help out.
from nearestneighbors.jl.
I don't have a plan by myself but I can see ways by which parallelization could move forward. I am only talking about shared memory here.
Threaded building of the trees
The gist in the first post should give an idea how it could be done. What I did was that one thread worked on the tree and then more threads take over as the tree is split.
Threaded querying of points
Should be fairly straightforward to just partition the query points and have one thread work on each partition.
from nearestneighbors.jl.
Here's a quick code to do parallel querying in case anyone needs something practical right now!
println("KNN Searches")
function simple_partition(n::Int,k::Int)
rval = ones(Int,k+1)
m,r = divrem(n,k)
fill!(rval, m)
rval[1] = 1
for i = 2:k+1
rval[i] += r .> (i-2)
end
cumsum!(rval,rval)
end
function parallel_knn(kdtree, X, k)
nt = Threads.nthreads()
idxs_t = Vector{Any}(nt)
dists_t = Vector{Any}(nt)
parts = simple_partition(size(X,2),nt)
Threads.@threads for i=1:nt
idxs_t[i], dists_t[i] = knn(kdtree, X[:,parts[i]:parts[i+1]-1], k, true)
end
return vcat(idxs_t...), vcat(dists_t...)
end
It passed my simple test on random points as giving the same things as knn on the full dataset. Otherwise, I haven't tested it at all.
Also, you'd probably want to make it pass more arguments to the actual knn function. I was just being lazy for my use.
Haven't tried to measure speedup! Just trying to use this to get some answers for a problem I don't want to wait forever for knn to compute.
from nearestneighbors.jl.
Related Issues (20)
- README.md Misleading Custom Metric Documentation
- Document that `inrangecount` also counts the point itself HOT 2
- [Question] Can you insert new data into an existing KDTree object? HOT 2
- Compilation time issues with very high dimensions HOT 3
- Reverse Cuthill-McKee ordering option HOT 1
- Querying number of distance evaluations HOT 3
- Make datatypes of the KNN results selectable for potentially lower memory overhead
- Does ball tree work with any metric? HOT 2
- Add example with `skip` option to documentation HOT 1
- Julia 1.10 is waiting on IO to finish during compilation HOT 3
- It should be possible to make `KDNode` smaller
- KDTree: Wrong results for non-Euclidean metrics
- Cannot build KDTree with Subarrays since v0.4.14 HOT 3
- KDTree with Matrix{ComplexF64} HOT 1
- Can't do `knn` on `AbstractVector{SVector}` HOT 2
- Test benchmarks and have them run on CI
- 1.0 road map HOT 5
- `get_min_distance_sq` seems weird
- Interface for tree traversal / walking of BallTree/KDTree HOT 3
- Trees for integer input data errors now it seems
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from nearestneighbors.jl.