Performance considerations (discussion)

Created by: portnov

While trying to improve performance of "bend along surface" node, I did some research about about whether it worth it to move some part of code to C++ (you can see the code at https://github.com/portnov/python_cpp_excersises). Here are the results:

• It is not so hard to integrate C++ and python nowadays. Keyword is "boost::python".
• It is possible to eliminate data copying almost completely in most situations.
• The most subtle thing is memory management. Python has it's own memory management, C++ has it's own "kind-of memory management". You must know at each moment who owns the particular pointer, who is gonna release it and when exactly.
• For non-numeric code (such as "get data and put it to the map of some sort", "traverse that map" and so on), C++ code is about 3-4 times faster than very similar python code.
• For vectorized numeric computations in C++, the keywords are "boost::ublas" and "eigen" (last one uses SIMD instructions when they are available).
• For numeric code, C++ can be about 10 times faster than python code.
• But, in many places, for numeric computations we already use numpy. Numpy is actually a C++ library with support of SIMD instructions. So, the performance of python-numpy code is about 1:1 to performance of C++ Eigen code (some functions are slower in numpy, other are slower in Eigen).

So my conclusions for now are:

• It does not worth rewriting LinearSpline/CubicSpline/Spline2D classes in C++, as they are actually mostly written in C++ already (by usage of numpy). The main way to improve performance of nodes that use these is to process lesser amounts of data in most cases.
• It could worth rewriting some code that processes a lot of data with non-numerical algorithms, in C++. But I do not know such places in Sverchok.
• If you see some node / class in Sverchok, that does some numerical computations, and you want to improve it's performance - the step 1 is to use numpy.

Will write some other thoughts later...