Faster Python Programs through Optimization

Mike Mueller (Python Academy)
Python
Location: D137/138
Please note: to attend, your registration must include Tutorials.
Average rating: **...
(2.88, 17 ratings)
  • 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

NOTE: Attendees must have Python 2.5 or 2.6 installed. Run the script and follow the
instructions here.

Photo of Mike Mueller

Mike Mueller

Python Academy

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.
When he does not program or teach, he spends time with his wife and his two
kids or works out and swims.

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Comments

Akkana Peck
07/22/2010 10:31pm PDT

The handout is very good—that was the main value of the tutorial.

Lex Berezhny
07/22/2010 8:56am PDT

It seemed that the presenter was just reading from the handout. The material was good and useful but overall the presentation was lacking.

Picture of John Fitzpatrick
John Fitzpatrick
07/20/2010 2:39pm PDT

Good basic ideas for improving the performance of Python programs. I program in Perl and Ruby - and not Python - and I was able to follow the material. It won’t give you the answers for your performance problems, but the ideas let you understand some of the internals of Python and start thinking for yourself about performance improvement.

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