Perhaps a better approach, in the long run, might be to includeĬppad atomic operations in the Cppad Swig interface. Recording the function would be slow, but once it is Would be to define the matrix operations in octave and have themīy element operators. This would be a rather severe limitation for Only overloads scalar operations, not matrix multiplication andĮlement-wise matrix operations (like e.g. Is there a package, similar to numpy, that works for Octave ?Īfter a look at the source, it seems to me GNU Octave Vectorization here does NOT equal Multi-threaded down on the metal.Operations for overloaded types. Multi-thread and multi-core is beyond the scope of this answer, the easiest way is to define your octave code to spawn 8 separate parallel job1.m through job8.m files that run all at the same time, have all of them process 1/8th of the task at hand, at the end wait for all to finish then rejoin the answer. If you want your sixteen-core CPU under your desk maxing out its CPU use to 100% while this operation is executing, then there is no escape: you must stop and re-define this problem in a multi-threaded and multi-core oriented way. Your CPU is twiddling its thumbs while this iteration occurs. The 4th addition must wait for the 3rd, and 3rd must wait for the 2nd. C code is still processing this code in an iterative first-in-first out queue-like way. Vectorized code like this is usually good-enough for typical tasks, but if extracting every rev of horsepower from your Alienware's QUAD-CHIP 32-Core hyperthreaded CPU is mission critical, keep reading.ĭon't make the mistake of saying this code is 'multi-threaded' or 'multi-core' because it's not. Operations performed in a vectorized way like this above are between 2 and 5 orders of magnitude faster than if you put this in a for loop. This arrayfun code is 'vectorized', and GNU Octave packages this code to be handed to pre-optimized C code rather than having GNU Octave manage its own glacial iteration.

Down straight to multi-core and multi-thread optimization on the bare metal:

PROTIP: Why this code on a sufficiently large matrix won't max out your quad-core CPU.īelow is an analysis of the arrayfun's capabilities from the perspective of Vectorization down through lowlevel C. Notice how the procedure (add 5) was applied to each item in the matrix. Put this code in filename called 'main.m' ) Put this code in filename called 'myfun.m' function = myfun(num)

Gnu octave matrix operations