Thread-parallel reduce(vcat, ...) performance issues when providing init

Hey all—

I’ve got a problem where I have a vec_of_vecs::Vector{Vector{T}} where the length is long and the length of each element is long. I’d love to use multithreading to do reduce(vcat, vec_of_vecs) more efficiently. From some benchmarking, it looks like Folds.jl provides a very convenient way to do this, and the parallel speedup of ThreadedEx() compared to SequentialEx() is seriously impressive and almost perfectly linear with the number of threads I provide (thanks @tkf!).

…But, the actual speed compared to reduce(vcat, vec_of_vecs) is two orders of magnitude slower, and I think the problem is having to provide the init kwarg. Because reduce(vcat, vec_of_vecs, init=T[]), or init=first(...), is similarly slow! Can anybody help me work out a sensible initialization to fix the speed disparity? Here is an example you can run:

# run with julia -O3 -t 3 on a 6-core CPU.

using Folds, BenchmarkTools, Serialization

const N = 5000
const M = 100
const vec_of_vecs = [rand(Int64, N) for _ in 1:M]
const (v1, vrest) = (vec_of_vecs[1], vec_of_vecs[2:end])

print("Serial time (no init, base):") # 127 μs, 2 alloc 
@btime reduce(vcat, $vec_of_vecs)

print("Serial time (init, base):") # 13.3 ms, 198 alloc
@btime reduce(vcat, $vrest, init=$v1)

print("Serial time (Folds):") # 13.48 ms, 198 alloc
@btime Folds.reduce(vcat, $vrest, SequentialEx(), init=$v1)

print("Thread-parallel time (Folds):") # 3.39 ms, 223 alloc
@btime Folds.reduce(vcat, $vrest, ThreadedEx(),   init=$v1)

The results for me were similar if I instead provided init=T[]. I would appreciate any thoughts people have—clearly Folds.jl is capable of really speeding things up, but just not in the way I’m trying to use it currently.

Base's reduce provides a manual specialization for reduce(vcat, _) without init. Actually executing reduce(vcat, _) would be extremely slow since it allocates a new array for each input element which is what is happening in Folds.reduce and reduce with init. A bit better approach here is to use append!!

julia> using BenchmarkTools, Folds, BangBang

julia> @btime reduce(vcat, $vec_of_vecs);
  265.167 μs (2 allocations: 3.81 MiB)

julia> Threads.nthreads()
4

julia> @btime Folds.reduce(vcat, $vec_of_vecs);
  3.074 ms (224 allocations: 64.39 MiB)

julia> @btime Folds.reduce(append!!, $vec_of_vecs);
  767.082 μs (50 allocations: 9.77 MiB)

julia> @btime Folds.reduce(vcat, $vec_of_vecs, SequentialEx());
  29.569 ms (200 allocations: 216.73 MiB)

julia> @btime Folds.reduce(append!!, $vec_of_vecs, SequentialEx());
  471.255 μs (9 allocations: 4.88 MiB)

I suspect that Folds.reduce(append!!, _, SequentialEx()) is slower than reduce(vcat, _) because the latter pre-compute the output array size. A similar optimization is possible in Folds.

I suspect that Folds.reduce(append!!, _s) is slower than Folds.reduce(append!!, _, SequentialEx()) because it invokes GC a lot.

Proper parallel implementation requires a two-pass approach: compute the output indices using parallel cumsum and then copy the elements in parallel. It’s not implemented in Folds yet.

1 Like

That all makes a ton of sense. I should have looked at more than just the docstrings for reduce, I apologize. I also should have just guessed that it would be doing something particularly clever with no init. I’ll definitely play with using append!!, but based on your thoughts here maybe I should just implement a slight manual methods and use a good old Folds.foreach(...) or something to more manually update a pre-allocated output.

Thanks again!

1 Like