Asynchronously storing results

New to Julia
1

I’ve been experimenting with the @sync and @async macros and I need some advice.
Basically, I have some (nested) embarrassingly parallel loops. The issue is, I want to store the calculations from each function call asynchronously. But I’m having some trouble on the storing part.

The following code should exemplify this:

function sleep_ij(i,j)
	res = i^2+j
	sleep(0.1*res)
	return res
end

@time begin
	res = Vector{Float64}(undef, 5)
	res_arr = fill(res, 2)
	@sync for i in 1:2
		@async res_arr[i] = [sleep_ij(i,j) for j in 1:5]
	end
	println(res_arr)
end
# Slow but correct results
# Array{Float64,1}[[2.0, 3.0, 4.0, 5.0, 6.0], [5.0, 6.0, 7.0, 8.0, 9.0]]
#  3.577137 seconds (122.19 k allocations: 6.380 MiB)

@time begin
	res = Vector{Float64}(undef, 5)
	res_arr = fill(res, 2)
	@sync for i in 1:2
		for j in 1:5
			@async res_arr[i][j] = sleep_ij(i,j)
		end
	end
	println(res_arr)
end
# Fast but wrong results
# Array{Float64,1}[[5.0, 6.0, 7.0, 8.0, 9.0], [5.0, 6.0, 7.0, 8.0, 9.0]]
#  0.912431 seconds (12.53 k allocations: 698.873 KiB)

@time begin
	res_arr = []
	@sync for i in 1:2
		for j in 1:5
			@async push!(res_arr, sleep_ij(i,j))
		end
	end
	println(res_arr)
end
# Fast but won't keep order (which is a must)
# Any[2, 3, 4, 5, 5, 6, 6, 7, 8, 9]
#  0.910911 seconds (12.21 k allocations: 684.984 KiB)

Is there any way I can achieve the fastest speeds shown above and at the same time fill my resulting array with the correct values?

2

Part of the problem is that you should not use fill to create an array of arrays. Each sub-array is a reference to the others. That is why the results are identical in each sub-array.

Is this what you are looking for?

function sleep_ij(i,j)
	res = i^2+j
	sleep(0.1*res)
	return res
end

@time begin
	res_arr = [Vector{Float64}(undef, 5) for _ in 1:2]
	@sync for i in 1:2
		for j in 1:5
			@async res_arr[i][j] = sleep_ij(i,j)
		end
	end
	println(res_arr)
end

Results:

Array{Float64,1}[[2.0, 3.0, 4.0, 5.0, 6.0], [5.0, 6.0, 7.0, 8.0, 9.0]]
  0.922813 seconds (56.22 k allocations: 2.803 MiB)
3 Likes
3

Oh that went straight over my head when trying to solve this.
Thank you very much @Christopher_Fisher!

1 Like