(octave.info)Basic Vectorization

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19.1 Basic Vectorization
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To a very good first approximation, the goal in vectorization is to
write code that avoids loops and uses whole-array operations.  As a
trivial example, consider

     for i = 1:n
       for j = 1:m
         c(i,j) = a(i,j) + b(i,j);
       endfor
     endfor

compared to the much simpler

     c = a + b;

This isn’t merely easier to write; it is also internally much easier to
optimize.  Octave delegates this operation to an underlying
implementation which, among other optimizations, may use special vector
hardware instructions or could conceivably even perform the additions in
parallel.  In general, if the code is vectorized, the underlying
implementation has more freedom about the assumptions it can make in
order to achieve faster execution.

   This is especially important for loops with "cheap" bodies.  Often it
suffices to vectorize just the innermost loop to get acceptable
performance.  A general rule of thumb is that the "order" of the
vectorized body should be greater or equal to the "order" of the
enclosing loop.

   As a less trivial example, instead of

     for i = 1:n-1
       a(i) = b(i+1) - b(i);
     endfor

write

     a = b(2:n) - b(1:n-1);

   This shows an important general concept about using arrays for
indexing instead of looping over an index variable.  Note: Index
Expressions.  Also use boolean indexing generously.  If a condition
needs to be tested, this condition can also be written as a boolean
index.  For instance, instead of

     for i = 1:n
       if (a(i) > 5)
         a(i) -= 20
       endif
     endfor

write

     a(a>5) -= 20;

which exploits the fact that ‘a > 5’ produces a boolean index.

   Use elementwise vector operators whenever possible to avoid looping
(operators like ‘.*’ and ‘.^’).  Note: Arithmetic Ops.  For simple
inline functions, the ‘vectorize’ function can do this automatically.

 -- : vectorize (FUN)
     Create a vectorized version of the inline function FUN by replacing
     all occurrences of ‘*’, ‘/’, etc., with ‘.*’, ‘./’, etc.

     This may be useful, for example, when using inline functions with
     numerical integration or optimization where a vector-valued
     function is expected.

          fcn = vectorize (inline ("x^2 - 1"))
             ⇒ fcn = f(x) = x.^2 - 1
          quadv (fcn, 0, 3)
             ⇒ 6

     See also: Note: inline, Note: formula,
     Note: argnames.

   Also exploit broadcasting in these elementwise operators both to
avoid looping and unnecessary intermediate memory allocations.  Note:
Broadcasting.

   Use built-in and library functions if possible.  Built-in and
compiled functions are very fast.  Even with an m-file library function,
chances are good that it is already optimized, or will be optimized more
in a future release.

   For instance, even better than

     a = b(2:n) - b(1:n-1);

is

     a = diff (b);

   Most Octave functions are written with vector and array arguments in
mind.  If you find yourself writing a loop with a very simple operation,
chances are that such a function already exists.  The following
functions occur frequently in vectorized code:

   • Index manipulation

        • find

        • sub2ind

        • ind2sub

        • sort

        • unique

        • lookup

        • ifelse / merge

   • Repetition

        • repmat

        • repelems

   • Vectorized arithmetic

        • sum

        • prod

        • cumsum

        • cumprod

        • sumsq

        • diff

        • dot

        • cummax

        • cummin

   • Shape of higher dimensional arrays

        • reshape

        • resize

        • permute

        • squeeze

        • deal