Why Pythran is great for Python users doing numerical computing?
Let’s recall that “Pythran is an ahead-of-time (AOT) compiler for a subset of the Python language, with a focus on scientific computing. It takes a Python module annotated with a few interface description and turns it into a native Python module with the same interface, but (hopefully) faster.”
Pythran is able to produce very efficient C++ code and binaries from high
level Numpy code. Broadcasting (for example
2*a**2 + 6*b*c, where
c are Numpy arrays) is very fast with
Pythran. If the algorithm is easier to express without loops, don’t write
Pythran always releases the GIL and can use SIMD instructions and OpenMP!
Why Transonic is great for Pythran users?
Python + Numpy + Pythran is a great combo to easily write highly efficient scientific programs and libraries.
To use Pythran, one needs to isolate the numerical kernels functions in modules that are compiled by Pythran. The C++ code produced by Pythran never uses the Python interpreter. It means that only a subset of what is doable in Python can be done in Pythran files. Some language features are not supported by Pythran (for example no classes) and most of the extension packages cannot be used in Pythran files (basically only Numpy and some Scipy functions).
Another cause of frustration for Python developers when using Pythran is related to manual writting of Pythran function signatures in comments, which can not be automated. Pythran uses C++ templates but Pythran users can not think with this concept. We would like to be able to express the templated nature of Pythran with modern Python syntax (in particular type annotations).
Finally, another limitation is that it is not possible to use Pythran for just-in-time (JIT) compilation so one needs to manually write all argument types.
With Transonic, we try to overcome these limitations. Transonic provides few supplementary Pythran commands and a small Python API to accelerate functions and methods with Pythran without writing the Pythran modules. The code of the numerical kernels can stay in the modules and in the classes where they were written. The Pythran files (i.e. the files compiled by Pythran), which are usually written by the user, are produced automatically by Transonic.
Bonus: There are Transonic syntaxes for both ahead-of-time and just-in-time compilations!
At run time, Transonic uses when possible the pythranized functions, but let’s stress again that codes using Transonic work fine without Pythran (of course without speedup)!
Implementation details: Under the hood, Transonic creates Pythran files (one per module for AOT compilation and one per function for JIT compilation) that can be compiled at build, import or run times depending of the cases. Note that the developers can still read the Pythran files if needed.
Transonic is really convenient for experimenting and benchmarking with Pythran (as for example these comparisons with Julia and with Numba):
The whole code can be gathered in one Python file.
@jitdecorator, we don’t need to add the types and to launch compilation commands!
@jit(i.e. with AOT compilation), it is easy to trigger a mode in which Transonic automatically takes care of all compilation steps (see Installation and configuration).