Thanks for the confirmation! Yes, I need more tests to see what the best
practice is for my particular problem.
On Monday, June 20, 2016 at 3:05:31 PM UTC+1, Chris Rackauckas wrote:
>
> Most likely. I would also time it with and without @simd at your problem
> size. For some reason I've had some simple loops do better without @simd.
>
> On Monday, June 20, 2016 at 2:50:22 PM UTC+1, chobb...@gmail.com wrote:
>>
>> Thanks! I'm still using v0.4.5. In this case, is the code I highlighted
>> above still the best choice for doing the job?
>>
>>
>> On Monday, June 20, 2016 at 1:57:25 PM UTC+1, Chris Rackauckas wrote:
>>>
>>> I think that for medium size (but not large) arrays in v0.5 you may want
>>> to use @threads from the threadding branch, and then for really large
>>> arrays you may want to use @parallel. But you'd have to test some timings.
>>>
>>> On Monday, June 20, 2016 at 11:38:15 AM UTC+1, chobb...@gmail.com wrote:
>>>>
>>>> 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
>>>>> ############################################################
>>>>>
>>>>>
