Package: transonic
Make your Python code fly attransonicspeeds!Documentation:https://transonic.readthedocs.ioTransonic is a pure Python package (requiring Python >= 3.9) to easily
accelerate modern Python-Numpy code with different accelerators (currentlyCython,PythranandNumba, but potentially laterCupy,PyTorch,JAX,Weld,Pyccel,Uarray, etc...).The accelerators are not hard dependencies of Transonic:Python codes using
Transonic run fine without any accelerators installed (of course without
speedup)![!WARNING]
Transonic is still in an active development stage (see ourroadmap).
Remarks and suggestions are very welcome.However, Transonic is now really usable, useful and used "in production" inFluidSimandFluidFFT(see examples forblocksand@boost).The long-term project[!NOTE]
The context of the creation of Transonic is presented in these documents:Transonic VisionMake your numerical Python code fly at transonic speed (EuroScipy 2019),Overview of the Python HPC landscape and zoom on Transonic.Transonic targets Python end-users and library developers.It is based on the following principles:We'd like to write scientific / computing applications / libraries with
pythonic, readable, modern code (Python >= 3.6).In some cases, Python-Numpy is too slow. However, there are tools to
accelerate such Python-Numpy code which lead to very good performances!Let's try to write universal code which express what we want to compute and
not the special hacks we want to use to make it fast. We just need nice ways
to express that a function, a method or a block of code has to be accelerated
(and how it has to be accelerated). We'd like to be able to do this in a
pythonic way, with decorators and context managers.There are many tools to accelerate Python-Numpy code! Let's avoid writting
code specialized for only one of these tools.Let's try to keep the code as it would be written without acceleration. For
example, with Transonic, we are able to accelerate (simple) methods of
classes even though some accelerators don't support classes.Let's accelerate/compile only what needs to be accelerated, i.e. only the
bottlenecks. Python and its interpreters are good for the rest. In most
cases, the benefice of writting big compiled extensions (with Cython or in
other languages) is negligible.Adding types is sometimes necessary. In modern Python, we have nice syntaxes
for type annotations! Let's use them.Ahead-of-time (AOT) and just-in-time (JIT) compilation modes are both useful.
We'd like to have a nice, simple and unified API for these two modes.AOT is useful to be able to distribute compiled packages and in some cases,
more optimizations can be applied.JIT is simpler to use (no need for type annotations) and optimizations can
be more hardware specific.Note that with Transonic, AOT compilers (Pythran and Cython) can be used as
JIT compilers (with a cache mechanism).To summarize, astrategy to quickly develop a very efficient scientific
application/librarywith Python could be:Use modern Python coding, standard Numpy/Scipy for the computations and all
the cool libraries you want.Profile your applications on real cases, detect the bottlenecks and apply
standard optimizations with Numpy.Add few lines of Transonic to compile the hot spots.What we have nowWe start to have a good API to accelerate Python-Numpy code (functions, methods
and blocks of code). The default Transonic backend uses Pythran and works well.Here, we explain why Pythran is so great for Python users and why Transonic is
great for Pythran users. There are
also (more experimental) backends for Cython and Numba.[!NOTE]
Transonic can be used in libraries and applications using MPI (asFluidSim).Installation and configurationpipinstalltransonicTransonic is sensible to environment variables:TRANSONIC_DIRcan be set to control where the cached files are
saved.TRANSONIC_DEBUGtriggers a verbose mode.TRANSONIC_COMPILE_AT_IMPORTcan be set to enable a mode for which
Transonic compiles at import time the Pythran file associated with the
imported module. This behavior can also be triggered programmatically
by using the functionset_compile_at_import.TRANSONIC_NO_REPLACEcan be set to disable all code replacements.
This is useful to compare execution times and when measuring code coverage.TRANSONIC_COMPILE_JITcan be set to false to disable the
compilation of jited functions. This can be useful for unittests.TRANSONIC_BACKENDto choose between the supported backends. The
default backend "pythran" is quite robust. There are now 3 other backends:
"cython", "numba" and "python" (prototypes).TRANSONIC_MPI_TIMEOUTsets the MPI timeout (default to 5 s).LicenseTransonic is distributed under the BSD License.