On Fri, 2016-09-02 at 13:34, Jong Wook Kim <[email protected]> wrote:
> Hi Yichao, what a nice idea :)
>
> But even if I write in the C++ way, @time sqrt(1) yields 5 allocations of 176
> bytes, and in inner loops this could be a bottleneck.
Those are just allocations for the return value of sqrt. Consider:
julia> function f(n)
out = 0.0
for i=1:n
out += sin(n)
end
out
end
f (generic function with 1 method)
julia> @time f(10) # warmup
0.000008 seconds (149 allocations: 10.167 KB)
-5.440211108893696
julia> @time f(10)
0.000005 seconds (5 allocations: 176 bytes)
-5.440211108893696
julia> @time f(10000)
0.000849 seconds (5 allocations: 176 bytes)
-3056.143888882987
> Is this an inevitable overhead of using ccall, or is it just a bogus that I
> can
> ignore?
>
> Jong Wook
>
>
> On Sep 2, 2016, at 7:14 AM, Yichao Yu <[email protected]> wrote:
>
>
>
> On Fri, Sep 2, 2016 at 7:03 AM, Jong Wook Kim <[email protected]> wrote:
>
> Hi,
>
> I'm using Julia 0.4.6 on OSX El Capitan, and was trying to normalize
> each column of matrix, so that the norm of each column becomes 1.
> Below
> is a isolated and simplified version of what I'm doing:
>
> function foo1()
> local a = rand(1000, 10000)
> @time for i in 1:size(a, 2)
> a[:, i] /= norm(a[:, i])
> end
> end
>
> foo1()
> 0.165662 seconds (117.44 k allocations: 232.505 MB, 37.08% gc time)
>
> I thought maybe the array copying is the problem, but this didn't help
> much:
>
> function foo2()
> local a = rand(1000, 10000)
> @time for i in 1:size(a, 2)
> a[:, i] /= norm(slice(a, :, i))
> end
> end
>
> foo2()
> 0.131377 seconds (98.47 k allocations: 155.921 MB, 36.66% gc time)
>
> and then I figured that this ugly one runs the fastest:
>
> function foo3()
> local a = rand(1000, 10000)
> @time for i in 1:size(a, 2)
> setindex!(a, norm(slice(a, :, i)), :, i)
> end
> end
>
> foo3()
> 0.013814 seconds (49.49 k allocations: 1.365 MB, 4.86% gc time)
>
> So I overheard a few times that plain for-loops are faster than
> vectorized code in Julia, and it seems it's allocating slightly less
> memory, but it's slower than the above.
>
> function foo4()
> local a = rand(1000, 10000)
> @time @inbounds for i in 1:size(a, 2)
> n = norm(slice(a, :, i))
> @inbounds for j in 1:size(a, 1)
> a[j, i] /= n
> end
> end
> end
>
> foo4()
> 0.055522 seconds (30.00 k allocations: 1.068 MB, 15.14% gc time)
>
> Is there a solution that is faster and less uglier than foo3() and
> foo4
> ()?
>
> Thinking of an equivalent implementation in C/C++, I should be able to
> write this logic without any heap allocation. Is it possible in Julia?
>
>
> You can write it in the way you'd write it in c++ and just don't use `norm
> `.
>
>
>
> Thanks,
> Jong Wook
