Speeding Up Logical Operations

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Hi Guys,
I am trying to uses the function below
function coords = distancePerBound(coords,L)
hL = L/2;
coords(coords > hL) = coords(coords > hL) - L;
coords(coords < -hL) = coords(coords < -hL) + L;
where coords is a 3xn (where n is ~100,000). In the function that uses it, this function gets called many times and is a bottle neck in speed. Is there any thing I can do to improve this?
On a side note, I know that for a vector 'x', x.*x is faster than x.^2. Is there any speeding possibility for 1./x as well? Thank you.

Accepted Answer

Matt J
Matt J on 9 Dec 2014
function coords = distancePerBound(coords,L)
hL = L/2;
idx=coords > hL;
coords(idx) = coords(idx) - L;
idx=coords < -hL;
coords(idx) = coords(idx) + L;
Carlos Adrian Vargas Aguilera
Well, after all you already get important info with >hL. Maybe
function coords = distancePerBound(coords,L)
hL = L/2;
idx = coords > hL;
coords(idx) = coords(idx) - L;
idx(~idx) = coords(~idx) >= -hL;
coords(~idx) = coords(~idx) + L;

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More Answers (1)

Adam on 9 Dec 2014
Edited: Adam on 9 Dec 2014
What does the profiler say about the speed of this?
Is it the function that is slow or the number of times you call it?
Within the function it is vectorised, but calling it many times lessens that effect because the many calls are not vectorised together.
Can you not concatenate the data input to the many calls to make just a single call?
Again though this comes down to where exactly the profiler is pointing to as being slow.
As an aside there are various alternatives that can be tried. If it were me and this function were found to be slow I would create a quick test script containing as many of those options as I can think of and see which is fastest because it is often hard to tell without simply trying and timing the different implementations.
Amit on 9 Dec 2014
Very true Adam. Actually that was what I was doing :P when I ran out of ideas, I thought to ask more experienced programmers here !!

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