Editing Python (programming language) (section)

===Performance===
A perforance comparison among various Python implementations, using a non-numerical (combinatorial) workload, was presented at EuroSciPy '13.<ref>{{cite conference |title=Performance of Python runtimes on a non-numeric scientific code |last=Murri |first=Riccardo |conference=European Conference on Python in Science (EuroSciPy) |year=2013 |arxiv=1404.6388|bibcode=2014arXiv1404.6388M}}</ref> In addition, Python's performance relative to other programming languages is benchmarked by [[The Computer Language Benchmarks Game]].<ref>{{cite web|title=The Computer Language Benchmarks Game|url=https://benchmarksgame-team.pages.debian.net/benchmarksgame/fastest/python.html|access-date=30 April 2020|archive-date=14 June 2020|archive-url=https://web.archive.org/web/20200614210246/https://benchmarksgame-team.pages.debian.net/benchmarksgame/fastest/python.html|url-status=live}}</ref>

There are several approaches to optimizing Python performance, given the inherent slowness of an [[interpreted language]]. These approaches include the following strategies or tools:

* [[Just-in-time compilation]]: Dynamically compiling Python code just before it is executed. This technique is used in libraries such as [[Numba]] and [[PyPy]].
* [[Compiler|Static compilation]]: Python code is compiled into machine code sometime before execution. An example of this approach is Cython, which compiles Python into C.
* Concurrency and parallelism: Multiple tasks can be run simultaneously. Python contains modules such as `multiprocessing` to support this form of parallelism. Moreover, this approach helps to overcome limitations of the [[Global interpreter lock|Global Interpreter Lock]] (GIL) in CPU tasks.
* Efficient data structures: Performance can also be improved by using data types such as <code>Set</code> for membership tests, or <code>deque</code> from <code>collections</code> for [[Queueing theory|queue]] operations.