On Thursday, March 12, 2015 at 2:14:34 AM UTC-7, Mauro wrote:
Julia is not yet very good with producing fast vectorized code which
does not allocate temporaries. The temporaries is what gets you here.
However, running your example, I get a slightly different a different
*.mem file
On Thursday, March 12, 2015 at 2:14:34 AM UTC-7, Mauro wrote:
Julia is not yet very good with producing fast vectorized code which
does not allocate temporaries. The temporaries is what gets you here.
However, running your example, I get a slightly different a different
*.mem file
On Thursday, March 12, 2015 10:31:21 AM Phil Tomson wrote:
Will this always be the case or is this a current limitation of the Julia
compiler? It seems like the more idiomatic, compact code should be handled
more efficiently. Having to break this out into nested for-loops definitely
hurts
you should be able to write:
@inbounds for y in 1:img.height
@simd for x in 1:img.wid
if 1 x img.wid
left = img.data[x-1,y]
center = img.data[x,y]
@inbounds right = img.data[x+1,y]
Just curious, why did you get rid of the @inbounds on the
For inplace matrix multipliation you can also try the in-place BLAS
operations:
http://docs.julialang.org/en/release-0.3/stdlib/math/?highlight=at_mul_b#Base.A_mul_B!
Am Donnerstag, 12. März 2015 10:14:34 UTC+1 schrieb Mauro:
Julia is not yet very good with producing fast vectorized code
