I have the same question regarding how to calculate the entry-wise vector
product and find this thread. As a novice, I wonder if the following code
snippet is still the standard for entry-wise vector multiplication that one
should stick to in practice? Thanks!
@fastmath @inbounds @simd for i=1:n
A[i] *= B[i]
end
On Tuesday, October 6, 2015 at 3:28:29 PM UTC+1, Lionel du Peloux wrote:
>
> Dear all,
>
> I'm looking for the fastest way to do element-wise vector multiplication
> in Julia. The best I could have done is the following implementation which
> still runs 1.5x slower than the dot product. I assume the dot product would
> include such an operation ... and then do a cumulative sum over the
> element-wise product.
>
> The MKL lib includes such an operation (v?Mul) but it seems OpenBLAS does
> not. So my question is :
>
> 1) is there any chance I can do vector element-wise multiplication faster
> then the actual dot product ?
> 2) why the built-in element-wise multiplication operator (*.) is much
> slower than my own implementation for such a basic linealg operation (full
> julia) ?
>
> Thank you,
> Lionel
>
> Best custom implementation :
>
> function xpy!{T<:Number}(A::Vector{T},B::Vector{T})
> n = size(A)[1]
> if n == size(B)[1]
> for i=1:n
> @inbounds A[i] *= B[i]
> end
> end
> return A
> end
>
> Bench mark results (JuliaBox, A = randn(300000) :
>
> function CPU (s) GC (%) ALLOCATION (bytes) CPU
> (x)
> dot(A,B) 1.58e-04 0.00 16 1.0
> xpy!(A,B) 2.31e-04 0.00 80
> 1.5
> NumericExtensions.multiply!(P,Q) 3.60e-04 0.00 80 2.3
> xpy!(A,B) - no @inbounds check 4.36e-04 0.00 80
> 2.8
> P.*Q 2.52e-03 50.36 2400512
> 16.0
> ############################################################
>
>
