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@node Vectorization and Faster Code Execution
@chapter Vectorization and Faster Code Execution
@cindex vectorization
@cindex vectorize

Vectorization is a programming technique that uses vector operations
instead of element-by-element loop-based operations. Besides frequently
writing more succinct code, vectorization also allows for better
optimization of the code. The optimizations may occur either in Octave's
own Fortran, C, or C++ internal implementation, or even at a lower level
depending on the compiler and external numerical libraries used to
build Octave. The ultimate goal is to make use of your hardware's vector
instructions if possible or to perform other optimizations in software.

Vectorization is not a concept unique to Octave, but is particularly
important because Octave is a matrix-oriented language. Vectorized
Octave code will see a dramatic speed up in most cases.

This chapter discusses vectorization and other techniques for faster
code execution.

@menu
* Basic Vectorization::        Basic techniques for code optimization
* Broadcasting::               Broadcasting operations
* Function Application::       Applying functions to arrays, cells, and structs
* Accumulation::               Accumulation functions
* Miscellaneous Techniques::   Other techniques for speeding up code
* Examples::
@end menu

@node Basic Vectorization
@section Basic Vectorization

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

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

@noindent
compared to the much simpler

@example
c = a + b;
@end example

@noindent
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 much 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, rather than

@example
@group
for i = 1:n-1
  a(i) = b(i+1) - b(i);
endfor
@end group
@end example

@noindent
write

@example
a = b(2:n) - b(1:n-1);
@end example

This shows an important general concept about using arrays for indexing
instead of looping over an index variable. @xref{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

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

@noindent
write

@example
a(a>5) -= 20;
@end example

@noindent
which exploits the fact that @code{a > 5} produces a boolean index.

Use elementwise vector operators whenever possible to avoid looping
(operators like @code{.*} and @code{.^}). @xref{Arithmetic Ops}. For
simple in-line functions, the @code{vectorize} function can do this
automatically.

@DOCSTRING(vectorize)

Also exploit broadcasting in these elementise operators both to avoid
looping and unnecessary intermediate memory allocations.
@xref{Broadcasting}.

Use built-in and library functions if possible. Built-in and compiled
functions are very fast. Even with a 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

@example
a = b(2:n) - b(1:n-1);
@end example

@noindent
is

@example
a = diff (b);
@end example

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:

@itemize @bullet
@item
Index manipulation

@itemize
@item
find
@item
sub2ind
@item
ind2sub
@item
sort
@item
unique
@item
lookup
@item
ifelse / merge
@end itemize

@item
Repetition
@itemize
@item
repmat
@item
repelems
@end itemize

@item
Vectorized arithmetic
@itemize
@item
sum
@item
prod
@item
cumsum
@item
cumprod
@item
sumsq
@item
diff
@item
dot
@item
cummax
@item
cummin
@end itemize

@item
Shape of higher dimensional arrays
@itemize
@item
reshape
@item
resize
@item
permute
@item
squeeze
@item
deal
@end itemize

@end itemize

@node Broadcasting
@section Broadcasting
@cindex broadcast
@cindex broadcasting
@cindex BSX
@cindex recycling
@cindex SIMD

Broadcasting refers to how Octave binary operators and functions behave
when their matrix or array operands or arguments differ in size. Since
version 3.6.0, Octave now automatically broadcasts vectors, matrices,
and arrays when using elementwise binary operators and functions.
Broadly speaking, smaller arrays are ``broadcast'' across the larger
one, until they have a compatible shape. The rule is that corresponding
array dimensions must either

@enumerate
@item
be equal or,
@item
one of them must be 1.
@end enumerate

@noindent
In case all dimensions are equal, no broadcasting occurs and ordinary
element-by-element arithmetic takes place. For arrays of higher
dimensions, if the number of dimensions isn't the same, then missing
trailing dimensions are treated as 1. When one of the dimensions is 1,
the array with that singleton dimension gets copied along that dimension
until it matches the dimension of the other array. For example, consider

@example
@group
x = [1 2 3;
     4 5 6;
     7 8 9];

y = [10 20 30];

x + y
@end group
@end example

@noindent
Without broadcasting, @code{x + y} would be an error because dimensions
do not agree. However, with broadcasting it is as if the following
operation were performed:

@example
@group
x = [1 2 3
     4 5 6
     7 8 9];

y = [10 20 30
     10 20 30
     10 20 30];

x + y
@result{}    11   22   33
      14   25   36
      17   28   39
@end group
@end example

@noindent
That is, the smaller array of size @code{[1 3]} gets copied along the
singleton dimension (the number of rows) until it is @code{[3 3]}. No
actual copying takes place, however. The internal implementation reuses
elements along the necessary dimension in order to achieve the desired
effect without copying in memory.

Both arrays can be broadcast across each other, for example, all
pairwise differences of the elements of a vector with itself:

@example
@group
y - y'
@result{}    0   10   20
    -10    0   10
    -20  -10    0
@end group
@end example

@noindent
Here the vectors of size @code{[1 3]} and @code{[3 1]} both get
broadcast into matrices of size @code{[3 3]} before ordinary matrix
subtraction takes place.

For a higher-dimensional example, suppose @code{img} is an RGB image of
size @code{[m n 3]} and we wish to multiply each colour by a different
scalar. The following code accomplishes this with broadcasting,

@example
img .*= permute ([0.8, 0.9, 1.2], [1, 3, 2]);
@end example

@noindent
Note the usage of permute to match the dimensions of the @code{[0.8,
0.9, 1.2]} vector with @code{img}.

For functions that are not written with broadcasting semantics,
@code{bsxfun} can be useful for coercing them to broadcast.

@DOCSTRING(bsxfun)

Broadcasting is only applied if either of the two broadcasting
conditions hold. As usual, however, broadcasting does not apply when two
dimensions differ and neither is 1:

@example
@group
x = [1 2 3
     4 5 6];
y = [10 20
     30 40];
x + y
@end group
@end example

@noindent
This will produce an error about nonconformant arguments.

Besides common arithmetic operations, several functions of two arguments
also broadcast. The full list of functions and operators that broadcast
is

@example
@group
      plus      +  .+
      minus     -  .-
      times     .*
      rdivide   ./
      ldivide   .\
      power     .^  .**
      lt        <
      le        <=
      eq        ==
      gt        >
      ge        >=
      ne        !=  ~=
      and       &
      or        |
      atan2
      hypot
      max
      min
      mod
      rem
      xor

      +=  -=  .+=  .-=  .*=  ./=  .\=  .^=  .**=  &=  |=
@end group
@end example

Beware of resorting to broadcasting if a simpler operation will suffice.
For matrices @var{a} and @var{b}, consider the following:

@example
c = sum (permute (a, [1, 3, 2]) .* permute (b, [3, 2, 1]), 3);
@end example

@noindent
This operation broadcasts the two matrices with permuted dimensions
across each other during elementwise multiplication in order to obtain a
larger 3d array, and this array is the summed along the third dimension.
A moment of thought will prove that this operation is simply the much
faster ordinary matrix multiplication, @code{c = a*b;}.

A note on terminology: ``broadcasting'' is the term popularized by the
Numpy numerical environment in the Python programming language. In other
programming languages and environments, broadcasting may also be known
as @emph{binary singleton expansion} (BSX, in @sc{Matlab}, and the
origin of the name of the @code{bsxfun} function), @emph{recycling} (R
programming language), @emph{single-instruction multiple data} (SIMD),
or @emph{replication}.

@subsection Broadcasting and Legacy Code

The new broadcasting semantics do not affect almost any code that worked
in previous versions of Octave without error. Thus for example all code
inherited from @sc{Matlab} that worked in previous versions of Octave
should still work without change in Octave. The only exception is code
such as

@example
@group
try
  c = a.*b;
catch
  c = a.*a;
end_try_catch
@end group
@end example

@noindent
that may have relied on matrices of different size producing an error.
Due to how broadcasting changes semantics with older versions of Octave,
by default Octave warns if a broadcasting operation is performed. To
disable this warning, refer to its ID (@pxref{doc-warning_ids}):

@example
warning ("off", "Octave:broadcast");
@end example

@noindent
If you want to recover the old behaviour and produce an error, turn this
warning into an error:

@example
warning ("error", "Octave:broadcast");
@end example

@node Function Application
@section Function Application
@cindex map
@cindex apply
@cindex function application

As a general rule, functions should already be written with matrix
arguments in mind and should consider whole matrix operations in a
vectorized manner. Sometimes, writing functions in this way appears
difficult or impossible for various reasons. For those situations,
Octave provides facilities for applying a function to each element of an
array, cell, or struct.

@DOCSTRING(arrayfun)
@DOCSTRING(spfun)
@DOCSTRING(cellfun)
@DOCSTRING(structfun)

@node Accumulation
@section Accumulation

Whenever it's possible to categorize according to indices the elements
of an array when performing a computation, accumulation functions can be
useful.

@DOCSTRING(accumarray)

@DOCSTRING(accumdim)

@node Miscellaneous Techniques
@section Miscellaneous Techniques
@cindex execution speed
@cindex speedups
@cindex optimization

Here are some other ways of improving the execution speed of Octave
programs.

@itemize @bullet

@item
Avoid computing costly intermediate results multiple times. Octave
currently does not eliminate common subexpressions. Also, certain
internal computation results are cached for variables. For instance, if
a matrix variable is used multiple times as an index, checking the
indices (and internal conversion to integers) is only done once.

@item
@cindex copy-on-write
@cindex COW
@cindex memory management
Be aware of lazy copies (copy-on-write). When a copy of an object is
created, the data is not immediately copied, but rather shared. The
actual copying is postponed until the copied data needs to be modified.
For example:

@example
@group
a = zeros (1000); # create a 1000x1000 matrix
b = a; # no copying done here
b(1) = 1; # copying done here
@end group
@end example

Lazy copying applies to whole Octave objects such as matrices, cells,
struct, and also individual cell or struct elements (not array
elements).

Additionally, index expressions also use lazy copying when Octave can
determine that the indexed portion is contiguous in memory. For example:

@example
@group
a = zeros (1000); # create a 1000x1000 matrix
b = a(:,10:100); # no copying done here
b = a(10:100,:); # copying done here
@end group
@end example

This applies to arrays (matrices), cell arrays, and structs indexed
using (). Index expressions generating cs-lists can also benefit of
shallow copying in some cases. In particular, when @var{a} is a struct
array, expressions like @code{@{a.x@}, @{a(:,2).x@}} will use lazy
copying, so that data can be shared between a struct array and a cell
array.

Most indexing expressions do not live longer than their `parent'
objects. In rare cases, however, a lazily copied slice outlasts its
parent, in which case it becomes orphaned, still occupying unnecessarily
more memory than needed. To provide a remedy working in most real cases,
Octave checks for orphaned lazy slices at certain situations, when a
value is stored into a "permanent" location, such as a named variable or
cell or struct element, and possibly economizes them. For example:

@example
@group
a = zeros (1000); # create a 1000x1000 matrix
b = a(:,10:100);  # lazy slice
a = []; # the original a array is still allocated
c@{1@} = b; # b is reallocated at this point
@end group
@end example

@item
Avoid deep recursion. Function calls to m-file functions carry a
relatively significant overhead, so rewriting a recursion as a loop
often helps. Also, note that the maximum level of recursion is limited.

@item
Avoid resizing matrices unnecessarily.  When building a single result
matrix from a series of calculations, set the size of the result matrix
first, then insert values into it.  Write

@example
@group
result = zeros (big_n, big_m)
for i = over:and_over
  r1 = @dots{}
  r2 = @dots{}
  result (r1, r2) = new_value ();
endfor
@end group
@end example

@noindent
instead of

@example
@group
result = [];
for i = ever:and_ever
  result = [ result, new_value() ];
endfor
@end group
@end example

Sometimes the number of items can't be computed in advance, and
stack-like operations are needed. When elements are being repeatedly
inserted at/removed from the end of an array, Octave detects it as stack
usage and attempts to use a smarter memory management strategy
pre-allocating the array in bigger chunks. Likewise works for cell and
struct arrays.

@example
@group
a = [];
while (condition)
  @dots{}
  a(end+1) = value; # "push" operation
  @dots{}
  a(end) = []; # "pop" operation
  @dots{}
endwhile
@end group
@end example

@item
Avoid calling @code{eval} or @code{feval} excessively, because
they require Octave to parse input or look up the name of a function in
the symbol table.

If you are using @code{eval} as an exception handling mechanism and not
because you need to execute some arbitrary text, use the @code{try}
statement instead.  @xref{The @code{try} Statement}.

@item
If you are calling lots of functions but none of them will need to
change during your run, set the variable
@code{ignore_function_time_stamp} to @code{"all"} so that Octave doesn't
waste a lot of time checking to see if you have updated your function
files.
@end itemize

@node Examples
@section Examples

Here goes a gallery of vectorization puzzles with solutions culled from
the help mailing list and the machine learning class...