Resolved: Using @async @sync macros to improve performance

Question:

I’m trying to parallelize this function by using @async @sync

function INSR_opt(f)
    function INSR0_opt(seq)
        len = length(seq)
        res = seq[end]
        @inbounds @sync for i in range(len-2,step=-1,stop=0)
            @async res = f([seq[i+1], res])
        end
        return res
    end
    return INSR0_opt
end

The way I’m using the macros seems correct to me but the performance just gets worse Without the macros:

122.962 μs (1073 allocations: 69.00 KiB)

With the macros:

154.681 μs (1091 allocations: 69.95 KiB)

I’ve even tried using @spawn instead of @async but the performance still won’t improve. I’ve checked the number of threads running with Threads.nthreads() and they are 4

Answer:

Your code is sequential, as you have a recursive dependence in res – so trying to parallelize it is both not possible and can lead to incorrect results. Essentially your code tries to re-implement foldr in a less efficient and non-generic way:

julia> INSR_opt(((a, b),) -> a => b)(1:4)
1 => (2 => (3 => 4))

julia> foldr(=>, 1:4)
1 => (2 => (3 => 4))

If you have better answer, please add a comment about this, thank you!

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