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
############################################################
