Prepare for ChunkSplitters 3.0

CHANGELOG.md

@@ -1,9 +1,16 @@
 OhMyThreads.jl Changelog
 =========================
 
+Version 0.7.0
+-------------
+- ![BREAKING][badge-breaking] We now use ChunkSplitters version 3.0. The function `OhMyThreads.chunks` has been renamed to `OhMyThreads.index_chunks`. The new functions `index_chunks` and `chunks` (different from the old one with the same name!) are now exported. See ChunkSplitters.jl for more information.
+- ![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()`.
+
 Version 0.6.2
 -------------
-- ![Enhancement][badge-enhancement] Added API support for `enumerate(chunks(...))`. Best used in combination with `chunking=false`.
+- ![Enhancement][badge-enhancement] Added API support for `enumerate(chunks(...))`. Best used in combination with `chunking=false`
 
 Version 0.6.1
 -------------

Project.toml

@@ -12,7 +12,7 @@ TaskLocalValues = "ed4db957-447d-4319-bfb6-7fa9ae7ecf34"
 [compat]
 Aqua = "0.8"
 BangBang = "0.3.40, 0.4"
-ChunkSplitters = "2.4"
+ChunkSplitters = "3"
 StableTasks = "0.1.5"
 TaskLocalValues = "0.1"
 Test = "1"

docs/src/literate/falsesharing/falsesharing.jl

@@ -30,11 +30,11 @@ data = rand(1_000_000 * nthreads());
 #
 # A common, manual implementation of this idea might look like this:
 
-using OhMyThreads: @spawn, chunks
+using OhMyThreads: @spawn, index_chunks
 
 function parallel_sum_falsesharing(data; nchunks = nthreads())
     psums = zeros(eltype(data), nchunks)
-    @sync for (c, idcs) in enumerate(chunks(data; n = nchunks))
+    @sync for (c, idcs) in enumerate(index_chunks(data; n = nchunks))
         @spawn begin
             for i in idcs
                 psums[c] += data[i]
@@ -102,7 +102,7 @@ nthreads()
 
 function parallel_sum_tasklocal(data; nchunks = nthreads())
     psums = zeros(eltype(data), nchunks)
-    @sync for (c, idcs) in enumerate(chunks(data; n = nchunks))
+    @sync for (c, idcs) in enumerate(index_chunks(data; n = nchunks))
         @spawn begin
             local s = zero(eltype(data))
             for i in idcs
@@ -131,7 +131,7 @@ end
 # using `map` and reusing the built-in (sequential) `sum` function on each parallel task:
 
 function parallel_sum_map(data; nchunks = nthreads())
-    ts = map(chunks(data, n = nchunks)) do idcs
+    ts = map(index_chunks(data, n = nchunks)) do idcs
         @spawn @views sum(data[idcs])
     end
     return sum(fetch.(ts))

docs/src/literate/falsesharing/falsesharing.md

@@ -39,11 +39,11 @@ catastrophic numerical errors due to potential rearrangements of terms in the su
 A common, manual implementation of this idea might look like this:
 
 ````julia
-using OhMyThreads: @spawn, chunks
+using OhMyThreads: @spawn, index_chunks
 
 function parallel_sum_falsesharing(data; nchunks = nthreads())
     psums = zeros(eltype(data), nchunks)
-    @sync for (c, idcs) in enumerate(chunks(data; n = nchunks))
+    @sync for (c, idcs) in enumerate(index_chunks(data; n = nchunks))
         @spawn begin
             for i in idcs
                 psums[c] += data[i]
@@ -132,7 +132,7 @@ into `psums` (once!).
 ````julia
 function parallel_sum_tasklocal(data; nchunks = nthreads())
     psums = zeros(eltype(data), nchunks)
-    @sync for (c, idcs) in enumerate(chunks(data; n = nchunks))
+    @sync for (c, idcs) in enumerate(index_chunks(data; n = nchunks))
         @spawn begin
             local s = zero(eltype(data))
             for i in idcs
@@ -168,7 +168,7 @@ using `map` and reusing the built-in (sequential) `sum` function on each paralle
 
 ````julia
 function parallel_sum_map(data; nchunks = nthreads())
-    ts = map(chunks(data, n = nchunks)) do idcs
+    ts = map(index_chunks(data, n = nchunks)) do idcs
         @spawn @views sum(data[idcs])
     end
     return sum(fetch.(ts))

docs/src/literate/mc/mc.jl

@@ -79,15 +79,15 @@ using OhMyThreads: StaticScheduler
 
 # ## Manual parallelization
 #
-# First, using the `chunks` function, we divide the iteration interval `1:N` into
+# First, using the `index_chunks` function, we divide the iteration interval `1:N` into
 # `nthreads()` parts. Then, we apply a regular (sequential) `map` to spawn a Julia task
 # per chunk. Each task will locally and independently perform a sequential Monte Carlo
 # simulation. Finally, we fetch the results and compute the average estimate for $\pi$.
 
-using OhMyThreads: @spawn, chunks
+using OhMyThreads: @spawn, index_chunks
 
 function mc_parallel_manual(N; nchunks = nthreads())
-    tasks = map(chunks(1:N; n = nchunks)) do idcs
+    tasks = map(index_chunks(1:N; n = nchunks)) do idcs
         @spawn mc(length(idcs))
     end
     pi = sum(fetch, tasks) / nchunks
@@ -104,7 +104,7 @@ mc_parallel_manual(N)
 # `mc(length(idcs))` is faster than the implicit task-local computation within
 # `tmapreduce` (which itself is a `mapreduce`).
 
-idcs = first(chunks(1:N; n = nthreads()))
+idcs = first(index_chunks(1:N; n = nthreads()))
 
 @btime mapreduce($+, $idcs) do i
     rand()^2 + rand()^2 < 1.0

docs/src/literate/mc/mc.md

@@ -112,16 +112,16 @@ using OhMyThreads: StaticScheduler
 
 ## Manual parallelization
 
-First, using the `chunks` function, we divide the iteration interval `1:N` into
+First, using the `index_chunks` function, we divide the iteration interval `1:N` into
 `nthreads()` parts. Then, we apply a regular (sequential) `map` to spawn a Julia task
 per chunk. Each task will locally and independently perform a sequential Monte Carlo
 simulation. Finally, we fetch the results and compute the average estimate for $\pi$.
 
 ````julia
-using OhMyThreads: @spawn, chunks
+using OhMyThreads: @spawn, index_chunks
 
 function mc_parallel_manual(N; nchunks = nthreads())
-    tasks = map(chunks(1:N; n = nchunks)) do idcs
+    tasks = map(index_chunks(1:N; n = nchunks)) do idcs
         @spawn mc(length(idcs))
     end
     pi = sum(fetch, tasks) / nchunks
@@ -151,7 +151,7 @@ It is faster than `mc_parallel` above because the task-local computation
 `tmapreduce` (which itself is a `mapreduce`).
 
 ````julia
-idcs = first(chunks(1:N; n = nthreads()))
+idcs = first(index_chunks(1:N; n = nthreads()))
 
 @btime mapreduce($+, $idcs) do i
     rand()^2 + rand()^2 < 1.0

docs/src/literate/tls/tls.jl

@@ -102,12 +102,12 @@ res ≈ res_naive
 # iterations (i.e. matrix pairs) for which this task is responsible.
 # Before we learn how to do this more conveniently, let's implement this idea of a
 # task-local temporary buffer (for each parallel task) manually.
-using OhMyThreads: chunks, @spawn
+using OhMyThreads: index_chunks, @spawn
 using Base.Threads: nthreads
 
 function matmulsums_manual(As, Bs)
     N = size(first(As), 1)
-    tasks = map(chunks(As; n = 2 * nthreads())) do idcs
+    tasks = map(index_chunks(As; n = 2 * nthreads())) do idcs
         @spawn begin
             local C = Matrix{Float64}(undef, N, N)
             map(idcs) do i

docs/src/literate/tls/tls.md

@@ -140,12 +140,12 @@ Before we learn how to do this more conveniently, let's implement this idea of a
 task-local temporary buffer (for each parallel task) manually.
 
 ````julia
-using OhMyThreads: chunks, @spawn
+using OhMyThreads: index_chunks, @spawn
 using Base.Threads: nthreads
 
 function matmulsums_manual(As, Bs)
     N = size(first(As), 1)
-    tasks = map(chunks(As; n = 2 * nthreads())) do idcs
+    tasks = map(index_chunks(As; n = 2 * nthreads())) do idcs
         @spawn begin
             local C = Matrix{Float64}(undef, N, N)
             map(idcs) do i

docs/src/refs/api.md

@@ -37,16 +37,25 @@ GreedyScheduler
 SerialScheduler
 ```
 
-## Non-Exported
+## Re-exported
+
+|                        |                                                                     |
+|------------------------|---------------------------------------------------------------------|
+| `OhMyThreads.chunks`   | see [ChunkSplitters.jl](https://juliafolds2.github.io/ChunkSplitters.jl/stable/references/#ChunkSplitters.chunks) |
+| `OhMyThreads.index_chunks`   | see [ChunkSplitters.jl](https://juliafolds2.github.io/ChunkSplitters.jl/stable/references/#ChunkSplitters.index_chunks) |
+
+## Public but not exported
 
 |                        |                                                                     |
 |------------------------|---------------------------------------------------------------------|
 | `OhMyThreads.@spawn`   | see [StableTasks.jl](https://github.com/JuliaFolds2/StableTasks.jl) |
 | `OhMyThreads.@spawnat` | see [StableTasks.jl](https://github.com/JuliaFolds2/StableTasks.jl) |
 | `OhMyThreads.@fetch`   | see [StableTasks.jl](https://github.com/JuliaFolds2/StableTasks.jl) |
 | `OhMyThreads.@fetchfrom` | see [StableTasks.jl](https://github.com/JuliaFolds2/StableTasks.jl) |
-| `OhMyThreads.chunks`   | see [ChunkSplitters.jl](https://juliafolds2.github.io/ChunkSplitters.jl/dev/references/#ChunkSplitters.chunks) |
 | `OhMyThreads.TaskLocalValue`   | see [TaskLocalValues.jl](https://github.com/vchuravy/TaskLocalValues.jl) |
+| `OhMyThreads.Split`   | see [ChunkSplitters.jl](https://juliafolds2.github.io/ChunkSplitters.jl/stable/references/#ChunkSplitters.Split) |
+| `OhMyThreads.Consecutive`   | see [ChunkSplitters.jl](https://juliafolds2.github.io/ChunkSplitters.jl/stable/references/#ChunkSplitters.Consecutive) |
+| `OhMyThreads.RoundRobin`   | see [ChunkSplitters.jl](https://juliafolds2.github.io/ChunkSplitters.jl/stable/references/#ChunkSplitters.RoundRobin) |
 
 
 ```@docs

src/OhMyThreads.jl

@@ -6,7 +6,12 @@ for mac in Symbol.(["@spawn", "@spawnat", "@fetch", "@fetchfrom"])
 end
 
 using ChunkSplitters: ChunkSplitters
+const index_chunks = ChunkSplitters.index_chunks
 const chunks = ChunkSplitters.chunks
+const Split = ChunkSplitters.Split
+const Consecutive = ChunkSplitters.Consecutive
+const RoundRobin = ChunkSplitters.RoundRobin
+export chunks, index_chunks
 
 using TaskLocalValues: TaskLocalValues
 const TaskLocalValue = TaskLocalValues.TaskLocalValue