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Our course High-Performance Computing with Pythonalso covers Cython, focussing on the integration with NumPy.

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.

Motivation¶

The Python programming language has been used successfully in a large number of application domains. Even performance critical applications, such as scientific computation frameworks or text processing applications, have been realized using Python in order to benefit from short development cycles and highly maintainable code.

However, the interpreted language also has its weaknesses. Tight algorithms in numerical computations and character processing often suffer from the overhead of object operations in arithmetic expressions or memory copies in string slicing. In high performance applications, the optimizations that can be performed at the language level may not be sufficient.

This is where the Cython programming language shows its strength. Cython is a general purpose programming language that forms a best-of-both-worlds cross between the Python language and the ubiquitous datatypes of the C/C++ language. It comes with an optimising compiler that translates Python code into C code for Python extension modules, and tightly adapts the generated code to the available static type information.

Cython code can be written as high-level Python code and manually optimised in well selected hot-spots by statically declaring data types or calling directly into external code written in C, C++ or compatible languages. This makes the entire range from simple, expressive Python code down to highly optimized, low-level C code available for programming in a single language.

The objective of this course is to get to know the Cython language, and to learn how to use it to speed up Python code by orders of magnitude. You will also learn how to wrap external C libraries to efficiently and comfortably use them from Python.

Course Content

Day 1: Cython for Speed¶

My first Cython extension¶

• using pyximport to quickly (re-)build extension modules
• using cython.inline() to compile code at runtime
• building extension modules with distutils

Speeding up Python code with Cython¶

• fast access to Python’s builtin types
• fast looping over Python iterables and C types
• string processing
• fast arithmetic
• incrementally optimizing Cython code
• multi-threading outside of the GIL (Global Interpreter Lock)

Interfacing with external C code¶

• calling into external C libraries
• building against C libraries
• writing Python wrapper APIs
• calling C functions across extension module boundaries

Day 2: Cython and NumPy¶

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

Case studies¶

The participants are encouraged to send in short code examples from their own experience that they would like to see running faster by using Cython. Based on general interest and practicality, one or two of these examples will be examined as a case study. These examples must be available to the teacher at least one week before the course, and must be short but complete executable examples, including sufficient input data for benchmarking. Please be aware that example code that requires a substantial amount of explanation or background knowledge about a specific application domain will not be accepted.

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.

Software¶

We use our online programing system that contains all needed software. There is no need to install any additional software. A modern internet browser and a decent internet connection will be enough.

Hardware for Open In-Person Trainings¶

For open trainings at our teaching center you can use your own laptop. Alternatively, we provide teaching computers. Please let us know if you need one in your registration form.

Course Material¶

Every participant receives comprehensive materials in PDF format that cover the whole course content as well as all source code.