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Currently: Source code for High Performance Python 1 tutorial at PyCon 2012 Originally: Source code for High Performance Computing tutorial at EuroPython 2011 firstname.lastname@example.org NOTE - the following notes still need updating for 2012... Description: The 4 hour tutorial will cover various ways of speeding up the provided Mandelbrot code with a variety of Python packages that let us go from bytecode to C, run on many CPUs and many machines and also use a GPU. The presentation for the tutorial should give the necessary background. All the files are in subdirectories and are independent of each other, the general pattern is: python mandelbrot.py 1000 1000 where "mandelbrot.py" might be named e.g. "pure_python.py" or "cython_numpy_loop.py", the first 1000 is the pixel width and height, the second 1000 is the number of iterations. 1000x1000px plots with 1000 iterations are pretty. Use the arguments "100 30" for a super quick test to validate that things are working (it makes a 100x100px image using only 30 iterations). The tutorial starts by using cProfile, RunSnakeRun and line_profiler to find the bottleneck, we then improve the code and add libraries to keep making things faster. Overview of the versions: pure_python: Python implementations for python and pypy cython_pure_python: a converstion of the python code using cython numpy_loop: a conversion of the python code using numpy vectors (but run without vector calls) cython_numpy_loop: as numpy_loop but compiled with cython numpy_vector: using vector calls on numpy vectors numpy_vector_numexpr: adding numexpr on the numpy vectors shedskin: minor conversion to get good speed using shedskin multiprocessing: using built-in multiprocessing module to run on all cores using pure python implementation parallelpython_pure_python: using parallelpython module to run across machines and cpus parallelpython_cython_pure_puthon: showing compiled cython version of pure_python running over machines pycuda: gpuarray, elementwisekernel and sourcemodule examples of numpy-like and C code on CUDA GPUs via python Note that the pure_python examples run fine using PyPy or Python 2.7.1. Blog write-up (TO FOLLOW) Versions of packages used to create this tutorial: Cython 0.14.1 numexpr 1.4.2 numpy 1.5.1 pyCUDA HEAD from git as of 14th June 2011 (with CUDA 4.0 drivers) PyPy 1.5 Python 2.7.2 ParallelPython 1.6.1 ShedSkin 0.7.1 UPDATES: Cython 0.15.1 pip install cython TO UPDATE!