Presenter

Mike Müller lives in Leipzig, Germany and works as a consultant, programmer and trainer. He programs scientific software in Python and other languages. He teaches Python since 2004 and since 2006 at the Python Academy. His courses cover introductions to Python as well as special topics such as extensions or thread programming. Being an engineer who also works in research projects, he use numerous scientific Python packages on a daily basis and, therefore, also offers a course Python for Scientist and Engineers. He is an author of PyModelData, a Python package design for reading, writing and converting data for scientific modeling. When he does not program or teach, he spends time with his wife and his two kids or works out in the gym and runs.

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)

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