How to vectorize a specific for-loop

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Paolo Binetti
Paolo Binetti on 9 Dec 2016
Commented: Jan on 19 Dec 2016
I am trying to vectorize the for-loop hereafter. Would you have any hint? Thank you
for i = 1 : numel(text)-k+1 % "text" is a string
pattern(i,:) = text(i:i+k-1);

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Accepted Answer

Jan on 9 Dec 2016
Edited: Jan on 9 Dec 2016
Before you start a vectorization, care for a pre-allocation:
n = numel(str) - k + 1;
pattern = repmat(' ', n, k); % <-- added
for ii = 1:n
pattern(ii,:) = str(ii:ii+k-1); % [EDITED, was: pattern(k, :)]
For a string with 10'000 characters and k=6 this 8 times faster already. But you can get much faster with a loop:
n = numel(str) - k + 1;
pattern = repmat(' ', n, k);
for ii = 1:k % <-- k instead of n
pattern(:, ii) = str(ii:ii+n-1); % <-- (:, ii) instead of (ii, :)
Now the values are copied to a continuous block of memory, which is much faster. In addition the loop is much shorter assumed that k << n.
[EDITED] For your amusement a vectorized version:
index = bsxfun(@plus, (1:numel(str) - k + 1).', 0:k-1);
pattern = str(index);
[EDITED 2] And if we are on the way, a C-MEX for completeness:
#include "mex.h"
void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
mwSize k, n, f;
mxChar *in, *out;
mwSize dims[2];
// Get inputs:
in = (mxChar *) mxGetData(prhs[0]);
k = (mwSize) mxGetScalar(prhs[1]);
// Create outputs:
n = mxGetNumberOfElements(prhs[0]) - k + 1;
dims[0] = n;
dims[1] = k;
plhs[0] = mxCreateCharArray(2, dims);
out = (mxChar *) mxGetData(plhs[0]);
// Copy data:
f = out + n * k;
while (out < f) {
memcpy(out, in++, n * sizeof(mxChar));
out += n;
Timings for Matlab 2015b, Win64, mean of 100 iterations:
str = repmat(' ', 1, 10000);
k = 6;
Original: 0.134 sec
Pre-allocation: 0.0178 sec
Vectorized: 0.000806 sec
Continuous: 0.000348 sec
C-MEX: 0.0000529 sec
Now use the profiler to find out, if this piece of code is still the bottleneck of the program. If this piece of code takes 1% of the processing time only, accelerating it by a factor of 2 let the total program run only 0.5% faster. Therefore it is most likely a waste of time.
[EDITED 2] Conclusion:
  • The continuous copying is the fastest M-version I can imagine.
  • Vectorizing wastes time with creating the large index. Do not overestimate vectorization, when you do not operate on complete matrices.
  • The C-MEX is 7 times faster than the continuous copy version.
  • Avoiding the creation of the large redundant array would be much more efficient. Better use str(ii:ii+k-1) in the code instead of the fluffy pattern matrix.
Jan on 19 Dec 2016
Pre-allocation is very important in all software projects. Example:
v = [];
for k = 1:1000
v(k) = k;
Now in each iteration Matlab creates a new array and copied the old data. Therefore Matlab has to reserve and write sum(1:1000)*8 bytes, which is >4MB, although the results has 8kB only.
C-Mex is an interface from Matlab to C-code. C is usually much slower during the processing, but extremely tedious during writing and debugging.
If you do not use the large array "pattern" fianlly, but stay at the dynamic indexing and use str(ii:ii+k-1), the code might run faster. But this depends on what you do with the pattern array later on.

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More Answers (1)

Roger Stafford
Roger Stafford on 9 Dec 2016
You might try the ‘hankel’ function:
n = numel(text);
nk = n-k+1;
pattern = hankel(text(1:nk),text(nk:n));

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