# Contiguous memory and relational operators

3 vues (au cours des 30 derniers jours)
Matlab2010 le 22 Fév 2013
I have a 2D matrix of floats called data (approx size 1E5 x 2E2) and wish to test some conditions many times (>1e9 times) eg
data(i:j, h) <= k, where k is a float
This process is a real bottleneck in my code according to the profiler.
Here the author (Jan) seems to suggest running permute.m to make the memory contiguous before applying the inequality operator.
I am unclear how to order the permutation though. What have I missed? (or is the permute trick only valid in dimensions above 2?)
tmp = permute(data(i:j, h) , orderVec); %where does orderVec come from???
tmp <= k %this is now fast
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James Tursa le 22 Fév 2013
The permute "trick" was mentioned because the other post had : for the trailing dimension. What is your exact situation for subscripting? Is it always of the form (i:j,h) with i, j, and h scalars but changing each iteration?
Matlab2010 le 5 Mar 2013
Modifié(e) : Matlab2010 le 5 Mar 2013
yes thats correct, it is always of the form (i:j,h) with the scalars i, j, and h changing each iteration.
does this help at all?

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### Réponse acceptée

Jan le 22 Fév 2013
The permute method is useful, when a large multi-dimensional array is indexed repeatedly. In the other thread it was P(M, 4, N) with large M and N. Then calling P(:,i,:) repeatedly consumes much more time than getting Q(:, :, i) after:
Q = permute(P, [1,3,2]);
In your case, the comparison could be performed once only:
comp = (data <= k);
And instead of comparing in a loop, the vector comp(i:j, h) can be used directly. But I assume this is not a dramatic improvement. More precise advices are possible if you post the relevant part of the code.
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