Faster Python Programs through Optimization
- Audience level:
- Best Practices/Patterns
March 7th 9 a.m. – 12:20 p.m.
This tutorial provides an overview of techniques to improve the performance of Python programs. The focus is on concepts such as profiling, difference of data structures and algorithms as well as a selection of tools and libraries that help to speed up Python.
This tutorial provides an overview of techniques to improve the
performance of Python programs. The focus is on concepts such
as profiling, diffrence of data structures and algorithms as well as
a selection of tools an libraries that help to speed up Python.
Python programmers who would like concepts to improve performance.
Programmers with good Python knowledge.
Please bring your laptop with the operating system of your choice (Linux, Mac OS X, Windows). In addition to Python 2.6 or 2.7, we need:
- RunSnakeRun (http://www.vrplumber.com/programming/runsnakerun)
- the Guppy_PE framework (http://guppy-pe.sourceforge.net)
- psyco (http://psyco.sourceforge.net, Python 2.6 only, version 1.5.2 or higher)
- pypy (http://pypy.org) and
- NumPy (http://numpy.scipy.org, version 1.2 or higher).
This is a hands-on course. Students are strongly encouraged to work along with the trainer at the interactive prompt. There will be exercises the students need to do on their own. Experience shows that this active involvement is essential for an effective learning.
- How fast is fast enough? (10 min)
- Optimization guidelines (10 min)
- Premature optimization
- Optimization rules
- Seven steps for incremental optimization
- Optimization strategy (30 min)
- Measuring in stones
- Profiling CPU usage
- Profiling memory usage
- Algorithms and Anti-patterns (40 min)
- String concatenation
- List and generator comprehensions
- The right data structure
- The example (5 min)
- Testing speed (5 min)
- Pure Python (15 min)
- Meet Psyco, the JIT (5 min)
- Using PyPy (15 min)
- NumPy for numeric arrays (10 min)
- Using multiple CPUs with multiprocessing (20 min)
- Combination of optimization strategies (10 min)
- Results of different example implementations (5 min)