Python for High Performance Computing ===================================== Aim --- Python is an increasingly popular choice for developing scientific code, mainly owing to its ease of use and availability of libraries for scientific computing. This course explores some of the solutions the Python programmer needs to adopt in order to obtain the code performance required for large scientific computations. First, the popular NumPy and SciPy libraries are revised, followed by an introduction to coupling Python with compiled language programming (C and Fortran) in order to accelerate performance critical code. Then, some of the options available for parallel programming, in order to exploit the availability of multiple cores on modern hardware and further improve code performance. Prerequisites ------------- This course will assume a level understanding of fundamental programming concepts (variables, loops, conditionals, functions as well as fundamentals of object-oriented programming). It will also assume familiarity with basic Python syntax, along with data structures such as lists and tuples. The course will start with a very brief recapitulation of basic Python features. For less experienced programmers, however, a concise introductory online course on python programming is available from https://www.codecademy.com . Software -------- The course is based on Python 2.7. We strongly the Anaconda distribution available from https://www.continuum.io/downloads This has all the packages that will be required for the main part of the course: Numpy, SciPy, Matplotlib, jupyter. In addition to that, the course needs * a C and a Fortran compiler (gcc and gfortran are recommended) * a MPI library * the mpi4py Python module Contents -------- The main contents is in the directories images ... contains images used by all notebooks notebooks ... contains the jupyter notebooks for the course practicals ... contains the practicals associated with the course Additional material is in additional python_hpc_artwork.pptx Course notebooks ---------------- The notebooks are all in the directory notebooks. They are lecture00-intro ... course introduction (general) lecture01-python ... introducing python lecture02-numpy ... introducing numpy lecture03-matplotlib ... introducing matplotlib lecture04-scipy ... introducing scipy lecture05-extensions ... (serial) C/Fortran extensions lecture06-cython ... introducing cython lecture07-summary-first ... summary of serial solutions tested on the 2D heat equation lecture08-intro-parallel ... course introduction (to parallel computing) lecture09-extensions-openmp ... (parallel) C/Fortran extensions multithreaded using OpenMP lecture10-numba ... introducing numba lecture11-summary-second ... summary of multithreaded solutions tested on the 2D heat equation lecture12-multiprocessing ... introducing multiprocessing lecture13-mpi4py ... introducing mpi4py lecture14-conclusion ... course wrap-up The Lectures ------------ Lectures are presented as jupyter notebooks. To run the notebooks, go to the topmost directory and run jupyter notebook or jupyter-notebook To choose a particular browser, the command is jupyter notebook --browser=firefox Use the web bowser to navigate to individual lectures (e.g. "lecture12-multiprocessing") and start the presentations.
CGATOxford/python-hpc
Fork of the training material for the course: "Python for HPC" originally created by http://www.archer.ac.uk/ and adapted by http://www.arc.ox.ac.uk/
Jupyter Notebook