I have the following Julia code and I would like to parallelize it.
using DistributedArrays
function f(x)
return x^2;
end
y = DArray[]
@parallel for i in 1:100
y[i] = f(i)
end
println(y)
The output is DistributedArrays.DArray[]
. I would like to have the value of y as follows: y=[1,4,9,16,...,10000]
Julia's multi-threading provides the ability to schedule Tasks simultaneously on more than one thread or CPU core, sharing memory. This is usually the easiest way to get parallelism on one's PC or on a single large multi-core server. Julia's multi-threading is composable.
The Julia language implements multithreading using task parallelism where tasks communicate in shared memory as described in their article Announcing composable multi-threaded parallelism in Julia. In task parallelism, a task refers to a computational task such as executing a function.
Parallel computing refers to the process of breaking down larger problems into smaller, independent, often similar parts that can be executed simultaneously by multiple processors communicating via shared memory, the results of which are combined upon completion as part of an overall algorithm.
You can use n-dimensional distributed array comprehensions:
First you need to add some more processes, either local or remote:
julia> addprocs(CPU_CORES - 1);
Then you must use DistributedArrays
at every one of the spawned processes:
julia> @everywhere using DistributedArrays
Finally you can use the @DArray
macro, like this:
julia> x = @DArray [@show x^2 for x = 1:10];
From worker 2: x ^ 2 = 1
From worker 2: x ^ 2 = 4
From worker 4: x ^ 2 = 64
From worker 2: x ^ 2 = 9
From worker 4: x ^ 2 = 81
From worker 4: x ^ 2 = 100
From worker 3: x ^ 2 = 16
From worker 3: x ^ 2 = 25
From worker 3: x ^ 2 = 36
From worker 3: x ^ 2 = 49
You can see it does what you expect:
julia> x
10-element DistributedArrays.DArray{Int64,1,Array{Int64,1}}:
1
4
9
16
25
36
49
64
81
100
Remember it works with an arbitrary number of dimensions:
julia> y = @DArray [@show i + j for i = 1:3, j = 4:6];
From worker 4: i + j = 7
From worker 4: i + j = 8
From worker 4: i + j = 9
From worker 2: i + j = 5
From worker 2: i + j = 6
From worker 2: i + j = 7
From worker 3: i + j = 6
From worker 3: i + j = 7
From worker 3: i + j = 8
julia> y
3x3 DistributedArrays.DArray{Int64,2,Array{Int64,2}}:
5 6 7
6 7 8
7 8 9
julia>
This is the most julian way to do what you intended IMHO.
We can look at macroexpand
output in order to see what's going on:
Note: this output has been slightly edited for readability, T
stands for:
DistributedArrays.Tuple{DistributedArrays.Vararg{DistributedArrays.UnitRange{DistributedArrays.Int}}}
julia> macroexpand(:(@DArray [i^2 for i = 1:10]))
:(
DistributedArrays.DArray(
(
#231#I::T -> begin
[i ^ 2 for i = (1:10)[#231#I[1]]]
end
),
DistributedArrays.tuple(DistributedArrays.length(1:10))
)
)
Which basically is the same as manually typing:
julia> n = 10; dims = (n,);
julia> DArray(x -> [i^2 for i = (1:n)[x[1]]], dims)
10-element DistributedArrays.DArray{Any,1,Array{Any,1}}:
1
4
9
16
25
36
49
64
81
100
julia>
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