speed up finding common values in multiple matrices

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Alexander on 23 Sep 2011
I have three same-sized matrices (approx. 2000x2000). I am iteratively evaluating these matrices thousands of times and desperately need to speed up the processing. Here's what I'm trying to do:
A is a logical matrix
B contains integer values
C is a logical matrix
For a given value z, I need to find where B==z and A==1, and change these locations in C to ones, or:
C(B==z & A == 1) = 1;
Any ideas on speeding up would be fantastic. I'm not seeing how. Many thanks in advance.
Fangjun Jiang on 23 Sep 2011
Do you mean you currently use C(B==z & A == 1) = 1; and it's not fast enough?

Walter Roberson on 23 Sep 2011
How "dense" is A? If there are relatively few locations set in A compared to the size of A, then
locs = find(A);
C(locs(B(locs)==z)) = true;
Note that if A is not changing, then you only need to compute locs once before you start the iterations.
Alexander on 24 Sep 2011
Yes, going the find(B==z) route sped this up quite a bit, about a 30% improvement over my original code. Good suggestion.

Fangjun Jiang on 23 Sep 2011
If A is a logical matrix, A==1 is not needed. If C is a logical matrix, use true/false at the right hand side.
C(B==z & A) = true;
I wonder if this will help.
Alexander on 24 Sep 2011
Yes, you are correct. In my code, A had turned into a double. Once A became a logical like it is supposed to, your suggestion improved the speed about 20% on my computer. Walter's suggestion is a bit faster though...since B is relatively sparse, the find(B==z) doesn;t cost much so the below is about 10% faster than your suggestion. Thanks.
locs = find(B==z);
C(locs(A(locs))) = true;

Derek O'Connor on 24 Sep 2011
Here are some tests on loopy alternatives to vectorized functions.
%-----------------------------------
function [z,A,B,C] = GenDat(m,n)
%
% Derek O'Connor 24 Sep 2011
%
B = ceil(m*n*rand(m,n)); % B is integer
A = round(rand(m,n)) == 1; % A & C are logical
C = A;
z = m*n*rand;
% -------- end GenDat ---------
%-------------------------------------------------------
function C = UpDate0(z,A,B)
[m,n] = size(A);
C(1:m,1:n) = false;
C(B==z & A == 1) = true; % = 1 slows this operation
%
%-----------------------------------------
function C = UpDateDOC1(z,A,B)
[m,n] = size(A);
A = A(:);
C(1:m,1:n) = false;
for i = 1:m
for j = 1:n
if B(i,j) == z
if A(i,j)
C(i,j) = true;
end
end % endif B
end % endfor j
end % endfor i
%-----------------------------------------
function C = UpdateDOC2(z,A,B)
[m,n] = size(A);
C(1:m,1:n) = false;
A = A(:);
B = B(:);
C = C(:);
for k = 1:m*n
if B(k) == z
if A(k)
C(k) = true;
end
end % endif B
end % endfor k
%-----------------------------------------
function C = UpdateWal1(z,A,B)
% Walter Roberson's function.
%
locs = find(A);
C(locs(B(locs)==z)) = true;
%-----------------------------------------
function C = UpDateWal2(z,A,B)
locs = find(B==z);
C(locs(A(locs))) = true;
%---------------------------------------------------------
Timing tests for n = [500 1000 2000 3000], run 10 times gave these normalized average times
Normalized Average Times
n UpDate0 UpDateDOC1 UpDateDOC2 UpDateWal1
-------------------------------------------------------
500 1.17 1 1.03 1.94
1000 1.15 1.14 1 2.35
2000 1.09 1.14 1 2.23
3000 1.14 1.14 1 2.32
-------------------------------------------------------
Actual(secs)3000 0.16 0.17 0.14 0.33
R2008a 64-bit, Windows 7, 2.33GHz
These results are for random A,B,z but I suspect these times will depend very much on the actual inputs.
As you can see, not much of a speed-up. Given that the matrices are dense and there is no pattern to the updates, it's hard for me to see how this operation can be speeded up.
Perhaps if you accumulated a vector of z values and did a single update for the vector then this should give a speed-up. But "batching" the z values may not be possible.
UPDATE 1
I overlooked Walter's second method, which is remarkably fast
Normalized Average Times
n UpDate0 UpDateDOC1 UpDateDOC2 UpDateWal1 UpDateWal2
------------------------------------------------------------
500 4.27 3.64 3.81 6.86 1
1000 3.31 3.16 2.71 7.09 1
2000 3.39 3.42 2.96 7.00 1
3000 3.34 3.43 2.87 6.83 1
------------------------------------------------------------
Actual(secs) 3000 0.17 0.17 0.14 0.34 0.05
This shows that vectorization can give great speed-ups, if you choose the right one.
UPDATE 2
This table shows the dramatic effect the JIT accelerator has on loopy functions
Times(Acc off)/Times(Acc on)
n UD0 DOC1 DOC2 Wal1 Wal2
--------------------------------------------
500 1.3 45.4 38.6 1.2 1.0
1000 1.6 41.1 40.5 1.0 1.1
2000 1.6 39.2 39.4 1.0 1.1
3000 1.6 38.4 38.9 1.0 1.0
--------------------------------------------
R2008a 64-bit, Windows 7, 2.33GHz
Walter Roberson on 25 Sep 2011
In theory, it depends entirely on the density of matches. The lower the number of matches, the smaller the result of find() and the fewer locations that need to be checked for the following comparison.
In practice, for the same density, one would expect the first variant, checking logical matrix (A) first, would be faster, as one would expect the comparison step followed by logical indexing by the result of the comparison, to be slower than plain logical indexing. Of course, expectations should be put to the timing test...