Appendix A External Code Interface

"The sum of human wisdom is not contained in any one language" —Ezra Pound

Octave is a fantastic language for solving many problems in science and engineering. However, it is not the only computer language and there are times when you may want to use code written in other languages. Good reasons for doing so include: 1) not re-inventing the wheel; existing function libraries which have been thoroughly tested and debugged or large scale simulation codebases are a good example, 2) accessing unique capabilities of a different language; for example the well-known regular expression functions of Perl (but don’t do that because regexp already exists in Octave).

Performance should generally not be a reason for using compiled extensions. Although compiled extensions can run faster, particularly if they replace a loop in Octave code, this is almost never the best path to take. First, there are many techniques to speed up Octave performance while remaining within the language. Second, Octave is a high-level language that makes it easy to perform common mathematical tasks. Giving that up means shifting the focus from solving the real problem to solving a computer programming problem. It means returning to low-level constructs such as pointers, memory management, mathematical overflow/underflow, etc. Because of the low level nature, and the fact that the compiled code is executed outside of Octave, there is the very real possibility of crashing the interpreter and losing work.

Before going further, you should first determine if you really need to bother writing code outside of Octave.

• Can I get the same functionality using the Octave scripting language alone?

  Even when a function already exists outside the language, it may be better to simply reproduce the behavior in an m-file rather than attempt to interface to the outside code.

• Is the code thoroughly optimized for Octave?

  If performance is an issue you should always start with the in-language techniques for getting better performance. Chief among these is vectorization (see Vectorization and Faster Code Execution) which not only makes the code concise and more understandable but improves performance (10X-100X). If loops must be used, make sure that the allocation of space for variables takes place outside the loops using an assignment to a matrix of the right size, or zeros.

• Does the code make as much use as possible of existing built-in library routines?

  These routines are highly optimized and many do not carry the overhead of being interpreted.

• Does writing a dynamically linked function represent a useful investment of your time, relative to staying in Octave?

  It will take time to learn Octave’s interface for external code and there will inevitably be issues with tools such as compilers.

With that said, Octave offers a versatile interface for including chunks of compiled code as dynamically linked extensions. These dynamically linked functions can be called from the interpreter in the same manner as any ordinary function. The interface is bi-directional and external code can call Octave functions (like plot) which otherwise might be very difficult to develop.

The interface is centered around supporting the languages C++, C, and Fortran. Octave itself is written in C++ and can call external C++/C code through its native oct-file interface. The C language is also supported through the mex-file interface for compatibility with MATLAB. Fortran code is easiest to reach through the oct-file interface.

Because many other languages provide C or C++ APIs it is relatively simple to build bridges between Octave and other languages. This is also a way to bridge to hardware resources which often have device drivers written in C.