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
>> ############################################################
>>
>>
