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### 19.6 Miscellaneous Techniques

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

- 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.
- 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:

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

  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:

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

  This applies to arrays (matrices), cell arrays, and structs indexed using ‘`()`’. Index expressions generating comma-separated lists can also benefit from shallow copying in some cases. In particular, when a is a struct array, expressions like `{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:

      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

- 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.
- 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

      result = zeros (big_n, big_m)
      for i = over:and_over
        ridx = …
        cidx = …
        result(ridx, cidx) = new_value ();
      endfor

  instead of

      result = [];
      for i = ever:and_ever
        result = [ result, new_value() ];
      endfor

  Sometimes the number of items can not be computed in advance, and stack-like operations are needed. When elements are being repeatedly inserted or removed from the end of an array, Octave detects it as stack usage and attempts to use a smarter memory management strategy by pre-allocating the array in bigger chunks. This strategy is also applied to cell and struct arrays.

      a = [];
      while (condition)
        …
        a(end+1) = value; # "push" operation
        …
        a(end) = []; # "pop" operation
        …
      endwhile

- Avoid calling `eval` or `feval` excessively. Parsing input or looking up the name of a function in the symbol table are relatively expensive operations.

  If you are using `eval` merely as an exception handling mechanism, and not because you need to execute some arbitrary text, use the `try` statement instead. See The try Statement.

- Use `ignore_function_time_stamp` when appropriate. If you are calling lots of functions, and none of them will need to change during your run, set the variable `ignore_function_time_stamp` to `"all"`. This will stop Octave from checking the time stamp of a function file to see if it has been updated while the program is being run.

Next: Examples, Previous: JIT Compiler, Up: Vectorization and Faster Code Execution [Contents][Index]