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Fast NumPy Processing with Cython

This course is given by a core developer of the Cython open-source project.

Target Audience

The course targets medium level to experienced Python programmers who want to break through the limits of Python performance. A basic understanding of the C language is helpful but not required. Basic understanding of Cython and NumPy as provided in the courses Fast Code with the Cython Compiler and Numerical Calculations with NumPy is necessary.

Motivation

NumPy and SciPy come with a broad set of high-level functionality that allows to express complex computational algorithms concisely and efficiently. However, in many cases, sequential operations on NumPy arrays introduce a considerable overhead. This can happen when arrays are unnecessarily being copied during an operation that does not work in-place, but also due to lacking CPU cache locality when large arrays are being traversed multiple times in a row. In both cases, Cython can provide a substantial speed-up by expressing algorithms more efficiently.

The main features that make Cython so attractive for NumPy users are its ability to access and process the arrays directly at the C level, and the native support for parallel loops based on the OpenMP compiler infrastructure. To work efficiently with arrays and other memory buffers, Cython has native syntax support for the Python buffer protocol, which allows C extensions (like NumPy or image processing libraries) to grant foreign code direct access to their internal data buffers.

Course Content

Use of Python's buffer interface from Cython code

directly accessing data buffers of other Python extensions
retrieving meta data about the buffer layout
setting up efficient memory views on external buffers

Implementing fast Cython loops over NumPy arrays

looping over NumPy exported buffers
implementing a simple image processing algorithm
using "fused types" (simple templating) to implement an algorithm once and run it efficiently on different C data types

Use of parallel loops to make use of multiple processing cores

building modules with OpenMP
processing data in parallel
speeding up an existing loop using OpenMP threads

Note: the part on parallel processing requires a C compiler that supports OpenMP, e.g. gcc starting with 4.2, preferably 4.4 or later. It should be readily available in recent installations of both Linux and MacOS-X. Note that recent versions of XCode use the "clang" compiler, which does not support OpenMP. On these systems, please install gcc separately and make sure it can be used from your CPython installation. Users of Microsoft Windows must install the C compiler that was used to build their Python installation, e.g. the VS2008 Express or MinGW for Python 2.7.

Course Duration

1 day

Exercises

The participants can follow all steps directly on their computers. There are exercises at the end of each unit providing ample opportunity to apply the freshly learned knowledge.

Course Material

Every participant receives comprehensive printed materials that cover the whole course content as wells a CD with all source codes and used software.

Recommended Module Combinations

Basic understanding of Cython and NumPy as provided in the courses Fast Code with the Cython Compiler and Numerical Calculations with NumPy is necessary.

This course is part of the Python Power Course, eight days of Python courses in a row.

You might be interested in the modules Advanced Python, Optimizing of Python Programs or Python Extensions with Other Languages as well.

You should have intermediate Python experience or attend the course Python for Programmers before taking this course.

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