Problem on benchmarking multi-thread code

General Usage Performance
1

Hello,
I am trying to benchmark function with thread parallelization and compare the result with non-parallelized version of function.

using Base.Threads

nx = 10000
a = zeros(nx)
b = rand(nx)
dindicies = 2:(nx-1)

function update!(A,B)
    for i=dindicies
        @inbounds A[i] = B[i] - B[i-1]
    end
end

function update_thread!(A,B)
    @threads for i=dindicies
        @inbounds A[i] = B[i] - B[i-1]
    end
end

I get the result with @btime

julia> @btime update!($a,$b)
  1.329 ms (68455 allocations: 1.20 MiB)

julia> @btime update_thread!($a,$b)
  5.796 μs (21 allocations: 2.81 KiB)

Why the non-parallelized version allocate far more memory while the parallelized version doesn’t?

2

Your functions use global variables. That might not be optimal.

2 Likes
3

I still not quite understand. Could you give me more hint? What make me confused is the big difference of allocations.

4

Try to declare all variables used within the function scope

function update!(A,B)
    nx = 10000
    dindicies = 2:(nx-1)
    for i=dindicies
        @inbounds A[i] = B[i] - B[i-1]
    end
end

See Performance Tips · The Julia Language for more details.