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9/9 Research Software Engineering with Python (COMP233) - Performance
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In this last episode of this course, I talk to Itamar Turner-Trauring who created the website PythonSpeed and spent a considerable time on finding ways to make Python code faster and more efficient. Python and its ecosystem also have great tools how you can measure performance.
Links:
- https://pythonspeed.com a set of articles and recommendations on how to improve your performance
- https://blog.sentry.io/python-performance-testing-a-comprehensive-guide/ a general blog post on performance testing
- https://uwpce-pythoncert.github.io/SystemDevelopment/profiling.html
- https://en.wikipedia.org/wiki/Computer_performance
- https://python-102.readthedocs.io/en/latest/performance.html
- https://docs.python.org/3/tutorial/datastructures.html
- https://www.green-algorithms.org
- https://doi.org/10.1145/356635.356640 Donald Knuth's paper on over optimisation
- https://wiki.python.org/moin/TimeComplexity
- https://blog.jetbrains.com/dataspell/2023/08/polars-vs-pandas-what-s-the-difference/ comparing Polars with Panda
Profiling tools
- https://pyinstrument.readthedocs.io/en/latest/
- https://docs.python.org/3/library/profile.html
- https://docs.python.org/3/library/time.html the time function in Python
- https://docs.python.org/3/library/timeit.html another function to measure time in Python
- https://jiffyclub.github.io/snakeviz/ a graphic profile viewer
- https://bloomberg.github.io/memray/ flexible memory profiler
- https://github.com/benfred/py-spy
- https://github.com/P403n1x87/austin-python The Python wrapper for the Austin profiler
This podcast is brought to you by the Advanced Research Computing Centre of the University College London, UK.
Producer and Host: Peter Schmidt
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