ChunkSplitters.jl

ChunkSplitters.jl facilitates the splitting of a given list of work items (of potentially uneven workload) into chunks that can be readily used for parallel processing. Operations on these chunks can, for example, be parallelized with Julia's multithreading tools, where separate tasks are created for each chunk. Compared to naive parallelization, ChunkSplitters.jl therefore effectively allows for more fine-grained control of the composition and workload of each parallel task.

Working with chunks and their respective indices also improves thread-safety compared to a naive approach based on threadid() indexing (see PSA: Thread-local state is no longer recommended).

Installation

Install with:

julia> import Pkg; Pkg.add("ChunkSplitters")

The chunks iterator

The main interface is the chunks iterator, and the enumeration of chunks, with enumerate.

chunks(array::AbstractArray; n::Int, split::Symbol=:batch)

This iterator returns a vector of ranges which indicates the range of indices of the input array for each given chunk. The split parameter is optional. If split == :batch, the ranges are consecutive (default behavior). If split == :scatter, the range is scattered over the array.

The different chunking variants are illustrated in the following figure:

For split=:batch, each chunk is "filled up" with work items one after another such that all chunks hold approximately the same number of work items (as far as possible). For split=:scatter, the work items are assigned to chunks in a round-robin fashion. As shown below, this way of chunking can be beneficial if the workload (i.e. the computational weight) for different items is uneven.

Basic interface

Let's first illustrate the chunks returned by chunks for the different chunking variants:

julia> using ChunkSplitters

julia> x = rand(7);

julia> for inds in chunks(x; n=3, split=:batch)
           @show inds
       end
inds = 1:1:3
inds = 4:1:5
inds = 6:1:7

julia> for inds in chunks(x; n=3, split=:scatter)
           @show inds
       end
inds = 1:3:7
inds = 2:3:5
inds = 3:3:6

The chunk indices can be retrieved with the enumerate function, which is specialized for the ChunkSplitters structure such that it works with @threads:

julia> using ChunkSplitters, Base.Threads

julia> x = rand(7);

julia> @threads for (ichunk, inds) in enumerate(chunks(x; n=3))
           @show ichunk, inds
       end
(ichunk, inds) = (1, 1:1:3)
(ichunk, inds) = (2, 4:1:5)
(ichunk, inds) = (3, 6:1:7)

Simple multi-threaded example

Now, let's demonstrate how to use chunks in a simple multi-threaded example:

julia> using BenchmarkTools

julia> using ChunkSplitters

julia> function sum_parallel(f, x; n=Threads.nthreads())
           t = map(chunks(x; n=n)) do inds
               Threads.@spawn sum(f, @view x[inds])
           end
           return sum(fetch.(t))
       end

julia> x = rand(10^8);

julia> Threads.nthreads()
12

julia> @btime sum(x -> log(x)^7, $x);
  1.353 s (0 allocations: 0 bytes)

julia> @btime sum_parallel(x -> log(x)^7, $x; n=Threads.nthreads());
  120.429 ms (98 allocations: 7.42 KiB)

Of course, chunks can also be used in conjunction with @threads (see below).

Shared buffers: using enumerate

If shared buffers are required, the enumeration of the buffers by chunk using enumerate is useful, to avoid using the id of the thread. For example, here we accumulate intermediate results of the sum in an array chunk_sums of length n, which is later reduced:

A simple @threads-based example:

julia> using ChunkSplitters, Base.Threads

julia> x = collect(1:10^5);

julia> n = nthreads();

julia> chunk_sums = zeros(Int, n);

julia> @threads for (ichunk, inds) in enumerate(chunks(x; n=n))
           chunk_sums[ichunk] += sum(@view x[inds])
       end

julia> sum(chunk_sums)
5000050000

Lower-level getchunk function

The package also provides a lower-level getchunk function:

getchunk(array::AbstractArray, ichunk::Int; n::Int, split::Symbol=:batch)

that returns the range of indices corresponding to the work items in the input array that are associated with chunk number ichunk.

For example, if we have an array of 7 elements, and the work on the elements is divided into 3 chunks, we have (using the default split == :batch option):

julia> using ChunkSplitters

julia> x = rand(7);

julia> getchunk(x, 1; n=3)
1:1:3

julia> getchunk(x, 2; n=3)
4:1:5

julia> getchunk(x, 3; n=3)
6:1:7

And using split = :scatter, we have:

julia> using ChunkSplitters 

julia> x = rand(7);

julia> getchunk(x, 1; n=3, split=:scatter)
1:3:7

julia> getchunk(x, 2; n=3, split=:scatter)
2:3:5

julia> getchunk(x, 3; n=3, split=:scatter)
3:3:6

Example: getchunk usage

julia> using BenchmarkTools

julia> using ChunkSplitters

julia> function sum_parallel_getchunk(f, x; n=Threads.nthreads())
           t = map(1:n) do ichunk
               Threads.@spawn begin
                   local inds = getchunk(x, ichunk; n=n)
                   sum(f, @view x[inds])
               end
           end
           return sum(fetch.(t))
       end

julia> x = rand(10^8);

julia> Threads.nthreads()
12

julia> @btime sum(x -> log(x)^7, $x);
  1.363 s (0 allocations: 0 bytes)

julia> @btime sum_parallel_getchunk(x -> log(x)^7, $x; n=Threads.nthreads());
  121.651 ms (100 allocations: 7.31 KiB)