Add ChannelLike iterator wrapper

`ChannelLike` wraps an indexable object such that it can be iterated by
concurrent tasks in a safe manner similar to a `Channel`. This is
instead of `Channel` in the chunked `GreedyScheduler`.

CHANGELOG.md

@@ -7,6 +7,8 @@ Version 0.7.0
 - ![BREAKING][badge-breaking] If you provide a `chunks` or `index_chunks` as input we now disable the internal chunking without a warning. Previously, we did show a warning unless you had set `chunking=false`. In contrast, we now throw an error when you set any incompatible chunking related keyword arguments.
 - ![Deprecation][badge-deprecation] The `split` options `:batch` and `:scatter` are now deprecated (they still work but will be dropped at some point). Use `:consecutive` and `:roundrobin`, respectively, instead.
 - ![Enhancement][badge-enhancement] The `split` keyword argument can now also be a `<: OhMyThreads.Split`. Compared to providing a `Symbol`, the former can potentially give better performance. For example, you can replace `:consecutive` by `OhMyThreads.Consecutive()` and `:roundrobin` by `OhMyThreads.RoundRobin()`.
+- ![Feature][badge-feature] `ChannelLike` is a new public (but not exported) type. `ChannelLike(itr)` provide a way to iterate over `itr` in a concurrency safe manner similar to `Channel`. See the docstring for more details. ([#121][gh-pr-121])
+- ![Enhancement][badge-enhancement] `ChannelLike` is used internally for the `GreedyScheduler` when `chunking=true`. This improves performance overall but it is especially noticeable when the number of chunks is large. ([#121][gh-pr-121])
 
 Version 0.6.2
 -------------
@@ -136,3 +138,4 @@ Version 0.2.0
 [gh-issue-25]: https://github.com/JuliaFolds2/OhMyThreads.jl/issues/25
 
 [gh-pr-5]: https://github.com/JuliaFolds2/OhMyThreads.jl/pull/5
+[gh-pr-121]: https://github.com/JuliaFolds2/OhMyThreads.jl/pull/121

docs/src/literate/tls/tls.jl

@@ -382,6 +382,13 @@ sort(res_nu) ≈ sort(res_channel_flipped)
 @btime matmulsums_perthread_channel_flipped($As_nu, $Bs_nu; ntasks = 2 * nthreads());
 @btime matmulsums_perthread_channel_flipped($As_nu, $Bs_nu; ntasks = 10 * nthreads());
 
+# In addition, OhMyThreads provides an iterator-wrapper type
+# [`OhMyThreads.ChannelLike`](@ref) which can be used in place of a `Channel`. If
+# the number of elements is large this can be more efficient since there is no
+# need to copy the elements into the `Channel`. Concretely, in the example above,
+# we could replace `Channel() do .. end` with
+# `OhMyThreads.ChannelLike(1:length(As))`.
+
 # ## Bumper.jl (only for the brave)
 #
 # If you are bold and want to cut down temporary allocations even more you can

docs/src/literate/tls/tls.md

@@ -490,6 +490,13 @@ Quick benchmark:
 
 ````
 
+In addition, OhMyThreads provides an iterator-wrapper type
+[`OhMyThreads.ChannelLike`](@ref) which can be used in place of a `Channel`. If
+the number of elements is large this can be more efficient since there is no
+need to copy the elements into the `Channel`. Concretely, in the example above,
+we could replace `Channel() do .. end` with
+`OhMyThreads.ChannelLike(1:length(As))`.
+
 ## Bumper.jl (only for the brave)
 
 If you are bold and want to cut down temporary allocations even more you can

docs/src/refs/api.md

@@ -61,4 +61,5 @@ SerialScheduler
 ```@docs
 OhMyThreads.WithTaskLocals
 OhMyThreads.promise_task_local
+OhMyThreads.ChannelLike
 ```

src/implementation.jl

@@ -1,7 +1,7 @@
 module Implementation
 
 import OhMyThreads: treduce, tmapreduce, treducemap, tforeach, tmap, tmap!, tcollect
-using OhMyThreads: @spawn, @spawnat, WithTaskLocals, promise_task_local
+using OhMyThreads: @spawn, @spawnat, WithTaskLocals, promise_task_local, ChannelLike
 using OhMyThreads.Tools: nthtid
 using OhMyThreads: Scheduler,
                    DynamicScheduler, StaticScheduler, GreedyScheduler,
@@ -207,6 +207,7 @@ function _tmapreduce(f,
         ntasks = min(length(first(Arrs)), ntasks_desired)
         ch_len = length(first(Arrs))
     end
+    # TODO: Use ChannelLike for iterators that support it. Dispatch on IndexLinear?
     ch = Channel{Tuple{eltype.(Arrs)...}}(ch_len; spawn = true) do ch
         for args in zip(Arrs...)
             put!(ch, args)
@@ -255,11 +256,9 @@ function _tmapreduce(f,
     ntasks_desired = scheduler.ntasks
     ntasks = min(length(chnks), ntasks_desired)
 
-    ch = Channel{typeof(first(chnks))}(length(chnks); spawn = true) do ch
-        for args in chnks
-            put!(ch, args)
-        end
-    end
+    # ChunkSplitters.IndexChunks support everything needed for ChannelLike
+    ch = ChannelLike(chnks)
+
     tasks = map(1:ntasks) do _
         # Note, calling `promise_task_local` here is only safe because we're assuming that
         # Base.mapreduce isn't going to magically try to do multithreading on us...

src/schedulers.jl

@@ -233,8 +233,9 @@ end
 """
     GreedyScheduler (aka :greedy)
 
-A greedy dynamic scheduler. The elements of the collection are first put into a `Channel`
-and then dynamic, non-sticky tasks are spawned to process the channel content in parallel.
+A greedy dynamic scheduler. The elements are put into a shared workqueue and dynamic,
+non-sticky, tasks are spawned to process the elements of the queue with each task taking a new
+element from the queue as soon as the previous one is done.
 
 Note that elements are processed in a non-deterministic order, and thus a potential reducing
 function **must** be [commutative](https://en.wikipedia.org/wiki/Commutative_property) in
@@ -249,10 +250,10 @@ some additional overhead.
     * Determines the number of parallel tasks to be spawned.
     * Setting `ntasks < nthreads()` is an effective way to use only a subset of the available threads.
 - `chunking::Bool` (default `false`):
-    * Controls whether input elements are grouped into chunks (`true`) or not (`false`) before put into the channel. This can improve the performance especially if there are many iterations each of which are computationally cheap.
+    * Controls whether input elements are grouped into chunks (`true`) or not (`false`) before put into the shared workqueue. This can improve the performance especially if there are many iterations each of which are computationally cheap.
     * If `nchunks` or `chunksize` are explicitly specified, `chunking` will be automatically set to `true`.
 - `nchunks::Integer` (default `10 * nthreads()`):
-    * Determines the number of chunks (that will eventually be put into the channel).
+    * Determines the number of chunks (that will eventually be put into the shared workqueue).
     * Increasing `nchunks` can help with [load balancing](https://en.wikipedia.org/wiki/Load_balancing_(computing)). For `nchunks <= nthreads()` there are not enough chunks for any load balancing.
 - `chunksize::Integer` (default not set)
     * Specifies the desired chunk size (instead of the number of chunks).

src/types.jl

@@ -71,3 +71,66 @@ promise_task_local(f::Any) = f
 function (f::WithTaskLocals{F})(args...; kwargs...) where {F}
     promise_task_local(f)(args...; kwargs...)
 end
+
+"""
+    ChannelLike(itr)
+
+This struct wraps an indexable object such that it can be iterated by concurrent tasks in a
+safe manner similar to a `Channel`.
+
+`ChannelLike(itr)` is conceptually similar to:
+```julia
+Channel{eltype(itr)}(length(itr)) do ch
+    foreach(i -> put!(ch, i), itr)
+end
+```
+i.e. creating a channel, `put!`ing all elements of `itr` into it and closing it. The
+advantage is that `ChannelLike` doesn't copy the data.
+
+# Examples
+```julia
+ch = OhMyThreads.ChannelLike(1:5)
+
+@sync for taskid in 1:2
+    Threads.@spawn begin
+        for i in ch
+            println("Task #\$taskid processing item \$i")
+            sleep(1 / i)
+        end
+    end
+end
+
+# output
+
+Task #1 processing item 1
+Task #2 processing item 2
+Task #2 processing item 3
+Task #2 processing item 4
+Task #1 processing item 5
+```
+
+Note that `ChannelLike` is stateful (just like a `Channel`), so you can't iterate over it
+twice.
+
+The wrapped iterator must support `firstindex(itr)::Int`, `lastindex(itr)::Int` and
+`getindex(itr, ::Int)`.
+"""
+mutable struct ChannelLike{T}
+    const itr::T
+    @atomic idx::Int
+    function ChannelLike(itr::T) where {T}
+        return new{T}(itr, firstindex(itr) - 1)
+    end
+end
+
+Base.length(ch::ChannelLike) = length(ch.itr)
+Base.eltype(ch::ChannelLike) = eltype(ch.itr)
+
+function Base.iterate(ch::ChannelLike, ::Nothing = nothing)
+    this = @atomic ch.idx += 1
+    if this <= lastindex(ch.itr)
+        return (@inbounds(ch.itr[this]), nothing)
+    else
+        return nothing
+    end
+end