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Merge pull request #454 from JuliaParallel/jps/datadeps
Add spawn_datadeps for OMP-like task model
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| Original file line number | Diff line number | Diff line change |
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| # Datadeps (Data Dependencies) | ||
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| For many programs, the restriction that tasks cannot write to their arguments | ||
| feels overly restrictive and makes certain kinds of programs (such as in-place | ||
| linear algebra) hard to express efficiently in Dagger. Thankfully, there is a | ||
| solution: `spawn_datadeps`. This function constructs a "datadeps region", | ||
| within which tasks are allowed to write to their arguments, with parallelism | ||
| controlled via dependencies specified via argument annotations. Let's look at | ||
| a simple example to make things concrete: | ||
|
|
||
| ```julia | ||
| A = rand(1000) | ||
| B = rand(1000) | ||
| C = zeros(1000) | ||
| add!(X, Y) = X .+= Y | ||
| Dagger.spawn_datadeps() do | ||
| Dagger.@spawn add!(InOut(B), In(A)) | ||
| Dagger.@spawn copyto!(Out(C), In(B)) | ||
| end | ||
| ``` | ||
|
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||
| In this example, we have two Dagger tasks being launched, one adding `A` into | ||
| `B`, and the other copying `B` into `C`. The `add!` task is specifying that | ||
| `A` is being only read from (`In` for "input"), and that `B` is being read | ||
| from and written to (`Out` for "output", `InOut` for "input and output"). The | ||
| `copyto` task, similarly, is specifying that `B` is being read from, and `C` | ||
| is only being written to. | ||
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||
| Without `spawn_datadeps` and `In`, `Out`, and `InOut`, the result of these | ||
| tasks would be undefined; the two tasks could execute in parallel, or the | ||
| `copyto!` could occur before the `add!`, resulting in all kinds of mayhem. | ||
| However, `spawn_datadeps` changes things: because we have told Dagger how our | ||
| tasks access their arguments, Dagger knows to control the parallelism and | ||
| ordering, and ensure that `add!` executes and finishes before `copyto!` | ||
| begins, ensuring that `copyto!` "sees" the changes to `B` before executing. | ||
|
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||
| There is another important aspect of `spawn_datadeps` that makes the above | ||
| code work: if all of the `Dagger.@spawn` macros are removed, along with the | ||
| dependency specifiers, the program would still produce the same results, | ||
| without using Dagger. In other words, the parallel (Dagger) version of the | ||
| program produces identical results to the serial (non-Dagger) version of the | ||
| program. This is similar to using Dagger with purely functional tasks and | ||
| without `spawn_datadeps` - removing `Dagger.@spawn` will still result in a | ||
| correct (sequential and possibly slower) version of the program. Basically, | ||
| `spawn_datadeps` will ensure that Dagger respects the ordering and | ||
| dependencies of a program, while still providing parallelism, where possible. | ||
|
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||
| But where is the parallelism? The above example doesn't actually have any | ||
| parallelism to exploit! Let's take a look at another example to see the | ||
| datadeps model truly shine: | ||
|
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||
| ```julia | ||
| # Tree reduction of multiple arrays into the first array | ||
| function tree_reduce!(op::Base.Callable, As::Vector{<:Array}) | ||
| Dagger.spawn_datadeps() do | ||
| to_reduce = Vector[] | ||
| push!(to_reduce, As) | ||
| while !isempty(to_reduce) | ||
| As = pop!(to_reduce) | ||
| n = length(As) | ||
| if n == 2 | ||
| Dagger.@spawn Base.mapreducedim!(identity, op, InOut(As[1]), In(As[2])) | ||
| elseif n > 2 | ||
| push!(to_reduce, [As[1], As[div(n,2)+1]]) | ||
| push!(to_reduce, As[1:div(n,2)]) | ||
| push!(to_reduce, As[div(n,2)+1:end]) | ||
| end | ||
| end | ||
| end | ||
| return As[1] | ||
| end | ||
|
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||
| As = [rand(1000) for _ in 1:1000] | ||
| Bs = copy.(As) | ||
| tree_reduce!(+, As) | ||
| @assert isapprox(As[1], reduce((x,y)->x .+ y, Bs)) | ||
| ``` | ||
|
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||
| In the above implementation of `tree_reduce!` (which is designed to perform an | ||
| elementwise reduction across a vector of arrays), we have a tree reduction | ||
| operation where pairs of arrays are reduced, starting with neighboring pairs, | ||
| and then reducing pairs of reduction results, etc. until the final result is in | ||
| `As[1]`. We can see that the application of Dagger to this algorithm is simple - | ||
| only the single `Base.mapreducedim!` call is passed to Dagger - yet due to the | ||
| data dependencies and the algorithm's structure, there should be plenty of | ||
| parallelism to be exploited across each of the parallel reductions at each | ||
| "level" of the reduction tree. Specifically, any two `Dagger.@spawn` calls | ||
| which access completely different pairs of arrays can execute in parallel, | ||
| while any call which has an `In` on an array will wait for any previous call | ||
| which has an `InOut` on that same array. | ||
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| Additionally, we can notice a powerful feature of this model - if the | ||
| `Dagger.@spawn` macro is removed, the code still remains correct, but simply | ||
| runs sequentially. This means that the structure of the program doesn't have to | ||
| change in order to use Dagger for parallelization, which can make applying | ||
| Dagger to existing algorithms quite effortless. |
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