Performance Optimization for Python
===================================

Basics
------

-  `Basic tips for optimizing the performace of your Python
   code <https://wiki.python.org/moin/PythonSpeed/PerformanceTips>`__

-  `Code for the book “High Performance Python” by Micha Gorelick and
   Ian Ozsvald with
   OReilly <https://github.com/mynameisfiber/high_performance_python>`__

Interfacing with C/C++
----------------------

-  `Interfacing with C from Scipy lecture
   notes <https://scipy-lectures.org/advanced/interfacing_with_c/interfacing_with_c.html#introduction>`__

   -  Very nice overview and examples of four approaches.

   1. `Python/C API <https://docs.python.org/3/c-api/index.html>`__

      -  `Extending Python3.7 with C or
         C++ <https://docs.python.org/3/extending/extending.html>`__

   2. `ctypes - A foreign function library for
      Python <https://docs.python.org/3/library/ctypes.html>`__

      -  It provides C compatible data types, and allows calling
         functions in DLLs or shared libraries. It can be used to wrap
         these libraries in pure Python.

   3. `SWIG <http://www.swig.org/exec.html>`__

      -  **SWIG** is an interface compiler that connects programs
         written in C and C++ with scripting languages such as Python

   4. `Cython - C-Extensions for Python <https://cython.org/>`__

      -  `Basic tutorial of
         Cython <https://cython.readthedocs.io/en/latest/src/tutorial/cython_tutorial.html>`__
      -  `Tutorial on how to use Cython to optimize Python code by
         Adrian
         Price-Whelan <https://github.com/adrn/cython-tutorial>`__
      -  `Cython tutorial by Pauli Virtanen from
         2011 <https://python.g-node.org/python-summerschool-2011/_media/materials/cython/cython-slides.pdf>`__

Only for C++
~~~~~~~~~~~~

-  `Boost.Python <https://www.boost.org/doc/libs/1_70_0/libs/python/doc/html/index.html>`__

   -  **Boost.Python**, a C++ library which enables seamless
      interoperability between C++ and the Python programming language.

-  `pybind11 - Seamless operability between C++11 and
   Python <https://github.com/pybind/pybind11>`__

   -  **pybind11** is a lightweight header-only library that exposes C++
      types in Python and vice versa, mainly to create Python bindings
      of existing C++ code.
   -  This is used by the **LSST** developers, please see the `DM
      Pybind11 style
      guide <https://developer.lsst.io/pybind11/style.html>`__ for
      details.

Using Just-in-Time (JIT) Compiler
---------------------------------

-  `pypy - a fast, compliant alternative implementation of the Python
   language <http://pypy.org/>`__

-  `numba - makes Python code fast <http://numba.pydata.org/>`__

   -  **Numba** is an open source JIT compiler that translates a subset
      of Python and **NumPy** code into fast machine code.
      \__Numba__only supports LLVM.
   -  **Numba** offers a range of options for parallelizing your code
      for CPUs and GPUs, often with only minor code changes.
   -  `Numpy supports in
      Numba <http://numba.pydata.org/numba-doc/0.15.1/numpy_support.html>`__
   -  **Numba** has a **vectorize** and **guvectorize** decorators that
      can be very useful.

-  `hope - A Python Just-In-Time compiler for astrophysical
   computations <https://github.com/jakeret/hope>`__

   -  **hope** is a specialized method-at-a-time JIT compiler written in
      Python for translating Python source code into C++ and compiles
      this at runtime.
   -  Has not been updated for three yeears.

Tutorial and notes
~~~~~~~~~~~~~~~~~~

-  `Optimizing Python in the Real World: NumPy, Numba, and the NUFFT by
   Jake
   VanderPlas <https://jakevdp.github.io/blog/2015/02/24/optimizing-python-with-numpy-and-numba/>`__

Making Numpy faster
~~~~~~~~~~~~~~~~~~~

-  `jax - GPU- and TPU-backed NumPy with differentiation and JIT
   compilation by Google <https://github.com/google/jax>`__

   -  JAX is Autograd and XLA, brought together for high-performance
      machine learning research.

-  `autograd - Efficiently computes derivatives of numpy
   code <https://github.com/HIPS/autograd>`__

Other packages
~~~~~~~~~~~~~~

-  `How to speed up Python by
   scikit-learn <https://scikit-learn.org/stable/developers/performance.html>`__

Parallel computing in Python
----------------------------

Tutorial
~~~~~~~~

-  `An introduction to parallel programming using Python’s
   multiprocessing
   module <https://sebastianraschka.com/Articles/2014_multiprocessing.html>`__
-  `Speed Up Your Algorithms Part
   3 — Parallel-ization <https://towardsdatascience.com/speed-up-your-algorithms-part-3-parallelization-4d95c0888748>`__

Software
~~~~~~~~

Common tools:
^^^^^^^^^^^^^

-  `mpi4py - Python bindings for
   MPI <https://github.com/mpi4py/mpi4py>`__
-  `joblib - a set of tools to provide lightweight pipelining in
   Python <https://github.com/joblib/joblib>`__
-  `loky - Robust and reusable Executor for
   joblib <https://github.com/tomMoral/loky>`__
-  `schwimmbad - A common interface to processing
   pools <https://github.com/adrn/schwimmbad>`__

More “Big Data” approach:
^^^^^^^^^^^^^^^^^^^^^^^^^

-  `Dask - Parallel computing with task
   scheduling <https://dask.org/>`__

   -  **Dask** provides advanced parallelism for analytics, enabling
      performance at scale for the tools you love
   -  **Dask** is open source and freely available. It is developed in
      coordination with other community projects like **Numpy**,
      **Pandas**, and **Scikit-Learn**.
   -  `Official Dask tutorial using Jupyter
      notebook <https://github.com/dask/dask-tutorial>`__