# Python Programming/Performance

Since Python is an interpreted language in its most commonly used
CPython implementation, it is many times slower in a variety of tasks
than the most commonly used compiled non-managed languages such as C and
C++; for some tasks, it is more than 100 times slower. CPython seems to
be on par with Perl, another interpreted language, slower on some tasks,
faster on other tasks.

Performance can be measured using benchmarks. Benchmarks are often far
from representative of the real-world usage and have to be taken with a
grain of salt. Some benchmarks are outright wrong in that non-idiomatic
code is used for a language, yielding avoidably low performance for the
language.

../PyPy/ is a just-in-time (JIT) compiler that
often runs faster than CPython. Another compiler that can lead to
greater speeds is Numba, which works for a subset of Python. Yet another
compiler is ../Cython/, not to be confused with
CPython.

## External links

-   Python 3 versus C gcc fastest
    programs,
    benchmarksgame-team.pages.debian.net
-   Python 3 versus Java fastest
    programs,
    benchmarksgame-team.pages.debian.net
-   Python 3 versus Go fastest
    programs,
    benchmarksgame-team.pages.debian.net
-   Perl vs Python 3 - Which programs are
    fastest?,
    benchmarksgame-team.pages.debian.net
-   Python speed center, speed.python.org
-   pypy speed center, speed.pypy.org
-   Python Interpreters Benchmarks,
    pybenchmarks.org
-   Performance of Python runtimes on a non-numeric scientific
    code by Riccardo Murri,
    arxiv.org
-   How To Make Python Run As Fast As
    Julia
    by Jean Francois Puget, ibm.com

