Merge branch 'master' into mp/greedybug

CHANGELOG.md

@@ -4,15 +4,19 @@ OhMyThreads.jl Changelog
 Version 0.5.0
 -------------
 
+- ![Feature][badge-feature] The parallel functions (e.g. tmapreduce etc.) now support `scheduler::Symbol` besides `scheduler::Scheduler`. To configure the selected scheduler (e.g. set `nchunks` etc.) one may now pass keyword arguments directly into the parallel functions (they will get passed on to the scheduler constructor). Example: `tmapreduce(sin, +, 1:10; chunksize=2, scheduler=:static)`. Analogous support has been added to the macro API: (Most) settings (`@set name = value`) will now be passed on to the parallel functions as keyword arguments (which then forward them to the scheduler constructor). Note that, to avoid ambiguity, we don't support this feature for `scheduler::Scheduler` but only for `scheduler::Symbol`.
 - ![Feature][badge-feature] Added a `SerialScheduler` that can be used to turn off any multithreading.
 - ![Feature][badge-feature] Added `OhMyThreads.WithTaskLocals` that represents a closure over `TaskLocalValues`, but can have those values materialized as an optimization (using `OhMyThreads.promise_task_local`)
 - ![Feature][badge-feature] In the case `nchunks > nthreads()`, the `StaticScheduler` now distributes chunks in a round-robin fashion (instead of either implicitly decreasing `nchunks` to `nthreads()` or throwing an error).
 - ![Feature][badge-feature] `@set init = ...` may now be used to specify an initial value for a reduction (only has an effect in conjuction with `@set reducer=...` and triggers a warning otherwise).
+- ![Enhancement][badge-enhancement] `SerialScheduler` and `DynamicScheduler` now support the keyword argument `ntasks` as an alias for `nchunks`.
 - ![Enhancement][badge-enhancement] Made `@tasks` use `OhMyThreads.WithTaskLocals` automatically as an optimization.
+- ![Enhancement][badge-enhancement] Uses of `@local` within `@tasks` no-longer require users to declare the type of the task local value, it can be inferred automatically if a type is not provided.
 - ![BREAKING][badge-breaking] The `DynamicScheduler` (default) and the `StaticScheduler` now support a `chunksize` argument to specify the desired size of chunks instead of the number of chunks (`nchunks`). Note that `chunksize` and `nchunks` are mutually exclusive. (This is unlikely to break existing code but technically could because the type parameter has changed from `Bool` to `ChunkingMode`.)
 - ![Breaking][badge-breaking] `DynamicScheduler` and `StaticScheduler` don't support `nchunks=0` or `chunksize=0` any longer. Instead, chunking can now be turned off via an explicit new keyword argument `chunking=false`.
 - ![BREAKING][badge-breaking] Within a `@tasks` block, task-local values must from now on be defined via `@local` instead of `@init` (renamed).
 - ![BREAKING][badge-breaking] The (already deprecated) `SpawnAllScheduler` has been dropped.
+- ![BREAKING][badge-breaking] The default value for `ntasks`/`nchunks` for `DynamicScheduler` has been changed from `2*nthreads()` to `nthreads()`. With the new value we now align with `@threads :dynamic`. The old value wasn't giving good load balancing anyways and choosing a higher value penalizes uniform use cases even more. To get the old behavior, set `nchunks=2*nthreads()`.
 - ![Bugfix][badge-bugfix] When using the `GreedyScheduler` in combination with `tmapreduce` (or functions that build upon it) there could be non-deterministic errors in some cases (small input collection, not much work per element, see [#82](https://github.com/JuliaFolds2/OhMyThreads.jl/issues/82)). These cases should be fixed now.
 
 Version 0.4.6

README.md

@@ -38,21 +38,26 @@ focus on [data parallelism](https://en.wikipedia.org/wiki/Data_parallelism), tha
 ## Example
 
 ```julia
-using OhMyThreads
+using OhMyThreads: tmapreduce, @tasks
+using BenchmarkTools: @btime
+using Base.Threads: nthreads
 
 # Variant 1: function API
-function mc_parallel(N; kw...)
-    M = tmapreduce(+, 1:N; kw...) do i
+function mc_parallel(N; ntasks=nthreads())
+    M = tmapreduce(+, 1:N; ntasks) do i
         rand()^2 + rand()^2 < 1.0
     end
     pi = 4 * M / N
     return pi
 end
 
 # Variant 2: macro API
-function mc_parallel_macro(N)
+function mc_parallel_macro(N; ntasks=nthreads())
     M = @tasks for i in 1:N
-        @set reducer=+
+        @set begin
+            reducer=+
+            ntasks=ntasks
+        end
         rand()^2 + rand()^2 < 1.0
     end
     pi = 4 * M / N
@@ -62,19 +67,17 @@ end
 N = 100_000_000
 mc_parallel(N) # gives, e.g., 3.14159924
 
-using BenchmarkTools
-
-@show Threads.nthreads()                                        # 5 in this example
-
-@btime mc_parallel($N; scheduler=DynamicScheduler(; nchunks=1))   # effectively using 1 thread
-@btime mc_parallel($N)                                          # using all 5 threads
+@btime mc_parallel($N; ntasks=1) # use a single task (and hence a single thread)
+@btime mc_parallel($N)           # using all threads
+@btime mc_parallel_macro($N)     # using all threads
 ```
 
-Timings might be something like this:
+With 5 threads, timings might be something like this:
 
 ```
-447.093 ms (7 allocations: 624 bytes)
-89.401 ms (66 allocations: 5.72 KiB)
+417.282 ms (14 allocations: 912 bytes)
+83.578 ms (38 allocations: 3.08 KiB)
+83.573 ms (38 allocations: 3.08 KiB)
 ```
 
 (Check out the full [Parallel Monte Carlo](https://juliafolds2.github.io/OhMyThreads.jl/stable/literate/mc/mc/) example if you like.)

docs/make.jl

@@ -28,7 +28,7 @@ makedocs(;
         ]
     ],
     repo = "https://github.com/JuliaFolds2/OhMyThreads.jl/blob/{commit}{path}#{line}",
-    format = Documenter.HTML(repolink = "https://github.com/JuliaFolds2/OhMyThreads.jl"))
+    format = Documenter.HTML(repolink = "https://github.com/JuliaFolds2/OhMyThreads.jl"; collapselevel = 1))
 
 if ci
     @info "Deploying documentation to GitHub"

docs/src/index.md

@@ -14,21 +14,26 @@ to add the package to your Julia environment.
 ### Basic example
 
 ```julia
-using OhMyThreads
+using OhMyThreads: tmapreduce, @tasks
+using BenchmarkTools: @btime
+using Base.Threads: nthreads
 
 # Variant 1: function API
-function mc_parallel(N; kw...)
-    M = tmapreduce(+, 1:N; kw...) do i
+function mc_parallel(N; ntasks=nthreads())
+    M = tmapreduce(+, 1:N; ntasks) do i
         rand()^2 + rand()^2 < 1.0
     end
     pi = 4 * M / N
     return pi
 end
 
 # Variant 2: macro API
-function mc_parallel_macro(N)
+function mc_parallel_macro(N; ntasks=nthreads())
     M = @tasks for i in 1:N
-        @set reducer=+
+        @set begin
+            reducer=+
+            ntasks=ntasks
+        end
         rand()^2 + rand()^2 < 1.0
     end
     pi = 4 * M / N
@@ -38,19 +43,17 @@ end
 N = 100_000_000
 mc_parallel(N) # gives, e.g., 3.14159924
 
-using BenchmarkTools
-
-@show Threads.nthreads()                                        # 5 in this example
-
-@btime mc_parallel($N; scheduler=DynamicScheduler(; nchunks=1))   # effectively using 1 thread
-@btime mc_parallel($N)                                          # using all 5 threads
+@btime mc_parallel($N; ntasks=1) # use a single task (and hence a single thread)
+@btime mc_parallel($N)           # using all threads
+@btime mc_parallel_macro($N)     # using all threads
 ```
 
-Timings might be something like this:
+With 5 threads, timings might be something like this:
 
 ```
-447.093 ms (7 allocations: 624 bytes)
-89.401 ms (66 allocations: 5.72 KiB)
+417.282 ms (14 allocations: 912 bytes)
+83.578 ms (38 allocations: 3.08 KiB)
+83.573 ms (38 allocations: 3.08 KiB)
 ```
 
 (Check out the full [Parallel Monte Carlo](@ref) example if you like.)

docs/src/literate/integration/integration.jl

@@ -29,7 +29,6 @@ end
 # interval, as a multiple of the number of available Julia threads.
 
 using Base.Threads: nthreads
-@show nthreads()
 
 N = nthreads() * 1_000_000
 
@@ -82,3 +81,5 @@ using BenchmarkTools
 
 # Because the problem is trivially parallel - all threads to the same thing and don't need
 # to communicate - we expect an ideal speedup of (close to) the number of available threads.
+
+nthreads()

docs/src/literate/integration/integration.md

@@ -46,13 +46,12 @@ interval, as a multiple of the number of available Julia threads.
 
 ````julia
 using Base.Threads: nthreads
-@show nthreads()
 
 N = nthreads() * 1_000_000
 ````
 
 ````
-5000000
+10000000
 ````
 
 Calling `trapezoidal` we do indeed find the (approximate) value of $\pi$.
@@ -101,6 +100,10 @@ end
 # end
 ````
 
+````
+trapezoidal_parallel (generic function with 1 method)
+````
+
 First, we check the correctness of our parallel implementation.
 
 ````julia
@@ -120,14 +123,22 @@ using BenchmarkTools
 ````
 
 ````
-  12.782 ms (0 allocations: 0 bytes)
-  2.563 ms (37 allocations: 3.16 KiB)
+  24.348 ms (0 allocations: 0 bytes)
+  2.457 ms (69 allocations: 6.05 KiB)
 
 ````
 
 Because the problem is trivially parallel - all threads to the same thing and don't need
 to communicate - we expect an ideal speedup of (close to) the number of available threads.
 
+````julia
+nthreads()
+````
+
+````
+10
+````
+
 ---
 
 *This page was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*

docs/src/literate/juliaset/juliaset.jl

@@ -111,12 +111,12 @@ img = zeros(Int, N, N)
 # the load balancing of the default dynamic scheduler. The latter divides the overall
 # workload into tasks that can then be dynamically distributed among threads to adjust the
 # per-thread load. We can try to fine tune and improve the load balancing further by
-# increasing the `nchunks` parameter of the scheduler, that is, creating more and smaller
-# tasks.
+# increasing the `ntasks` parameter of the scheduler, that is, creating more tasks with
+# smaller per-task workload.
 
 using OhMyThreads: DynamicScheduler
 
-@btime compute_juliaset_parallel!($img; scheduler=DynamicScheduler(; nchunks=N)) samples=10 evals=3;
+@btime compute_juliaset_parallel!($img; ntasks=N, scheduler=:dynamic) samples=10 evals=3;
 
 # Note that while this turns out to be a bit faster, it comes at the expense of much more
 # allocations.
@@ -126,4 +126,4 @@ using OhMyThreads: DynamicScheduler
 
 using OhMyThreads: StaticScheduler
 
-@btime compute_juliaset_parallel!($img; scheduler=StaticScheduler()) samples=10 evals=3;
+@btime compute_juliaset_parallel!($img; scheduler=:static) samples=10 evals=3;

docs/src/literate/juliaset/juliaset.md

@@ -121,9 +121,9 @@ img = zeros(Int, N, N)
 ````
 
 ````
-nthreads() = 5
-  138.157 ms (0 allocations: 0 bytes)
-  40.373 ms (67 allocations: 6.20 KiB)
+nthreads() = 10
+  131.295 ms (0 allocations: 0 bytes)
+  31.422 ms (68 allocations: 6.09 KiB)
 
 ````
 
@@ -135,17 +135,17 @@ As stated above, the per-pixel computation is non-uniform. Hence, we do benefit
 the load balancing of the default dynamic scheduler. The latter divides the overall
 workload into tasks that can then be dynamically distributed among threads to adjust the
 per-thread load. We can try to fine tune and improve the load balancing further by
-increasing the `nchunks` parameter of the scheduler, that is, creating more and smaller
-tasks.
+increasing the `ntasks` parameter of the scheduler, that is, creating more tasks with
+smaller per-task workload.
 
 ````julia
 using OhMyThreads: DynamicScheduler
 
-@btime compute_juliaset_parallel!($img; scheduler=DynamicScheduler(; nchunks=N)) samples=10 evals=3;
+@btime compute_juliaset_parallel!($img; ntasks=N, scheduler=:dynamic) samples=10 evals=3;
 ````
 
 ````
-  31.751 ms (12011 allocations: 1.14 MiB)
+  17.438 ms (12018 allocations: 1.11 MiB)
 
 ````
 
@@ -158,11 +158,11 @@ To quantify the impact of load balancing we can opt out of dynamic scheduling an
 ````julia
 using OhMyThreads: StaticScheduler
 
-@btime compute_juliaset_parallel!($img; scheduler=StaticScheduler()) samples=10 evals=3;
+@btime compute_juliaset_parallel!($img; scheduler=:static) samples=10 evals=3;
 ````
 
 ````
-  63.147 ms (37 allocations: 3.26 KiB)
+  30.097 ms (73 allocations: 6.23 KiB)
 
 ````
 

docs/src/literate/mc/mc.jl

@@ -74,8 +74,8 @@ using Base.Threads: nthreads
 
 using OhMyThreads: StaticScheduler
 
-@btime mc_parallel($N) samples=10 evals=3;
-@btime mc_parallel($N; scheduler = StaticScheduler()) samples=10 evals=3;
+@btime mc_parallel($N; scheduler=:dynamic) samples=10 evals=3; # default
+@btime mc_parallel($N; scheduler=:static) samples=10 evals=3;
 
 # ## Manual parallelization
 #

docs/src/literate/mc/mc.md

@@ -34,7 +34,7 @@ mc(N)
 ````
 
 ````
-3.14145748
+3.14171236
 ````
 
 ## Parallelization with `tmapreduce`
@@ -69,7 +69,7 @@ mc_parallel(N)
 ````
 
 ````
-3.14134792
+3.14156496
 ````
 
 Let's run a quick benchmark.
@@ -86,9 +86,9 @@ using Base.Threads: nthreads
 ````
 
 ````
-nthreads() = 5
-  317.745 ms (0 allocations: 0 bytes)
-  88.384 ms (66 allocations: 5.72 KiB)
+nthreads() = 10
+  301.636 ms (0 allocations: 0 bytes)
+  41.864 ms (68 allocations: 5.81 KiB)
 
 ````
 
@@ -100,13 +100,13 @@ and compare the performance of static and dynamic scheduling (with default param
 ````julia
 using OhMyThreads: StaticScheduler
 
-@btime mc_parallel($N) samples=10 evals=3;
-@btime mc_parallel($N; scheduler=StaticScheduler()) samples=10 evals=3;
+@btime mc_parallel($N; scheduler=:dynamic) samples=10 evals=3; # default
+@btime mc_parallel($N; scheduler=:static) samples=10 evals=3;
 ````
 
 ````
-  88.222 ms (66 allocations: 5.72 KiB)
-  88.203 ms (36 allocations: 2.98 KiB)
+  41.839 ms (68 allocations: 5.81 KiB)
+  41.838 ms (68 allocations: 5.81 KiB)
 
 ````
 
@@ -121,7 +121,7 @@ simulation. Finally, we fetch the results and compute the average estimate for $
 using OhMyThreads: @spawn, chunks
 
 function mc_parallel_manual(N; nchunks = nthreads())
-    tasks = map(chunks(1:N; n = nchunks)) do idcs # TODO: replace by `tmap` once ready
+    tasks = map(chunks(1:N; n = nchunks)) do idcs
         @spawn mc(length(idcs))
     end
     pi = sum(fetch, tasks) / nchunks
@@ -132,7 +132,7 @@ mc_parallel_manual(N)
 ````
 
 ````
-3.1414609999999996
+3.14180504
 ````
 
 And this is the performance:
@@ -142,7 +142,7 @@ And this is the performance:
 ````
 
 ````
-  64.042 ms (31 allocations: 2.80 KiB)
+  30.224 ms (65 allocations: 5.70 KiB)
 
 ````
 
@@ -161,8 +161,8 @@ end samples=10 evals=3;
 ````
 
 ````
-  88.041 ms (0 allocations: 0 bytes)
-  63.427 ms (0 allocations: 0 bytes)
+  41.750 ms (0 allocations: 0 bytes)
+  30.148 ms (0 allocations: 0 bytes)
 
 ````