Guidelines for Optimization

Python programs are not principally slow. On the contrary: the execution speed is sufficient for many application. The basic question is if the program serves its intended purpose. Furthermore, other factors such as database access or network connections may determine the speed of the application.

The course provides important guidelines for optimization.

Profiling

Profiling can be used to find the spots of a program that use up most of the time.

The built-in modules profile, hotshot, and timeit are used in the course. Examples of common cases will help to understand how to use them. Participants are encouraged to bring their own programs for profiling to the course.

Algorithms and Anti-Patterns

Typically the larges gains of performance can be achieved through algorithmic changes.

Algorithms that are especially slow or fast in Python are shown. The preferred use of data structures are covered as well as the use of functional programming features of Python.

Psyco

Psyco is a so called "just-in-time-complier" (JIT), which allows to translate parts of the byte code to machine code. This may yield considerable speed improvements.

Examples are used to show the different possibilities of using Psyco. Participants are encouraged to bring their own programs to the course to optimize them.

Numerical Calculations with numpy

Numerical calculations using large list are rather slow in Python. The dynamical typing can use up lots of performance. The library numpy offers efficient processing of numerical arrays. Functionality and usage are inspired by MATLAB.

The basic possibilities of numpy are covered in the course.

Overview of Extensions to Python with Other Languages

The speed of Python programs can be increased through extensions written in other languages. An overview of possibilities to extend Python are given if this module is not combined with the module Python-Extensions with Other Languages.