Ah, good point. Though I guess that won't work til 0.6 since .* won't
auto-fuse yet?
Sent from my iPhone
> On 2 Nov. 2016, at 12:55, Chris Rackauckas <rackd...@gmail.com> wrote:
>
> This is pretty much obsolete by the . fusing changes:
>
> A .= A.*B
>
> should be an in-place update of A scaled by B (Tomas' solution).
>
>> On Tuesday, November 1, 2016 at 4:39:15 PM UTC-7, Sheehan Olver wrote:
>> Should this be added to a package? I imagine if the arrays are on the GPU
>> (AFArrays) then the operation could be much faster, and having a consistent
>> name would be helpful.
>>
>>
>>> On Wednesday, October 7, 2015 at 1:28:29 AM UTC+11, 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
>>> ############################################################
