Faster Python Programs through Optimization (Mike Müller)

Although Python programs may be slow for certain types of tasks, there are

many different ways to improve performance. This tutorial will introduce

optimization strategies and demonstrate techniques to implement them.

Another of the objectives of this course is to give participants the ability

to decide what might be the optimal solution for a certain performance problem. Participants are strongly recommended to bring laptops because all techniques

are introduced with examples and exercises.

Intended Audience

Python programmers with intermediate knowledge.

Class Outline

How Fast is Fast Enough?
Optimization Guidelines
- Premature optimization
- Optimization rules
- Seven steps for incremental optimization
Optimization strategy
- Measuring in stones
- Profiling CPU usage
- Profiling memory usage
Algorithms and Anti-patterns
- String Concatenation
- List and Generator Comprehensions
- The Right Data Structure
- Caching
The Example
Testing Speed
Pure Python
Meet Psyco, the JIT
Numpy for Numeric Arrays
Using multiple CPUs with pyprocessing/multiprocessing
Combination of optimization strategies
Results of Different Example Implementations

Requirements

All participants should bring laptops with Linux, Windows, or Mac OS.

Python 2.6, 2.5, or Python 2.4 need to be installed as well as an

editor or IDE.

The following third-party packages are needed:

psyco (version 1.5.2 or higher)
numpy (version 1.2 or higher)
pyprocessing (2.5 or lower only)
Guppy_PE framework