Fast sequential, threaded, and distributed for-loops for Julia—fold for humans™
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April 2020

FLoops: fold for humans™

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FLoops.jl provides a macro @floop. It can be used to generate a fast generic sequential and parallel iteration over complex collections.

Furthermore, the loop written in @floop can be executed with any compatible executors. See FoldsThreads.jl for various thread-based executors that are optimized for different kinds of loops. FoldsCUDA.jl provides an executor for GPU. FLoops.jl also provide a simple distributed executor.

Update notes

FLoops.jl 0.2 defaults to a parallel loop; i.e., it uses a parallel executor (e.g., ThreadedEx) when the executor is not specified and the explicit sequential form @floop begin ... end is not used.

That is to say, @floop without @reduce such as

@floop for i in eachindex(ys, xs)
    ys[i] = f(xs[i])

is now executed in parallel by default.


Parallel loop

@floop is a superset of Threads.@threads (see below) and in particular supports complex reduction with additional syntax @reduce:

julia> using FLoops  # exports @floop macro

julia> @floop for (x, y) in zip(1:3, 1:2:6)
           a = x + y
           b = x - y
           @reduce s += a
           @reduce t += b
       (s, t)
(15, -3)

For more examples, see parallel loops tutorial.

Sequential (single-thread) loop

Simply wrap a for loop and its initialization part with @floop begin ... end:

julia> @floop begin
           s = 0
           for x in 1:3
               s += x

For more examples, see sequential loops tutorial.

Advantages over Threads.@threads

@floop is a superset of Threads.@threads and has a couple of advantages:

  • @floop supports various input collection types including arrays, dicts, sets, strings, and many iterators from Base.Iterators such as zip and product. More precisely, @floop can generate high-performance parallel iterations for any collections that supports SplittablesBase.jl interface.
  • With FoldsThreads.NondeterministicEx, @floop can even parallelize iterations over non-parallelizable input collections (although it is beneficial only for heavier workload).
  • FoldsThreads.jl provides multiple alternative thread-based executors (= loop execution backend) that can be used to tune the performance without touching the loop itself.
  • FoldsCUDA.jl provides a simple GPU executor.
  • @reduce syntax for supporting complex reduction in a forward-compatible manner
    • Note: threadid-based reduction (that is commonly used in conjunction with @threads) may not be forward-compatible to Julia that supports migrating tasks across threads.
  • There is a trick for "changing" the effective number of threads without restarting julia using the basesize option.

The relative disadvantages may be that @floop is much newer than Threads.@threads and has much more flexible internals. These points can contribute to undiscovered bugs.

How it works

@floop works by converting the native Julia for loop syntax to foldl defined by Transducers.jl. Unlike foldl defined in Base, foldl defined by Transducers.jl is powerful enough to cover the for loop semantics and more.