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Python stands to lose its GIL, and gain a lot of speed

A new project to change the CPython runtime to boost multithreaded
performance has drawn the attention of Python's core development
team.

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Serdar Yegulalp By Serdar Yegulalp

Senior Writer, InfoWorld |

fabio ballasina (CC0)

One of Python's long-standing weaknesses, its inability to scale well
in multithreaded environments, is the target of a new proposal among
the core developers of the popular programming language.

Developer Sam Gross has proposed a major change to the Global
Interpreter Lock, or GIL--a key component in CPython, the reference
implementation of Python.

[ Also on InfoWorld: 11 tips for speeding up Python programs ]

If accepted, Gross's proposal would rewrite the way Python serializes
access to objects in its runtime from multiple threads, and would
boost multithreaded performance significantly.

The GIL has long been seen as an obstacle to better multithreaded
performance in CPython (and thus Python generally). Many efforts have
been made to remove it over the years, but at the cost of hurting
single-threaded performance--in other words, by making the vast
majority of existing Python applications slower.

Python's current metaphors for dealing with threading and
multiprocessing don't make it impossible to achieve high parallelism.
But they make it hard enough that developers often turn to
third-party modules like Dask to get that job done.

The new proposal makes changes to the way reference counting works
for Python objects, so that references from the thread that owns an
object are handled differently from those coming from other threads.

The overall effect of this change, and a number of others with it,
actually boosts single-threaded performance slightly--by around 10%,
according to some benchmarks performed on a forked version of the
interpreter versus the mainline CPython 3.9 interpreter.
Multithreaded performance, on some benchmarks, scales almost linearly
with each new thread in the best case--e.g., when using 20 threads, an
18.1x speedup on one benchmark and a 19.8x speedup on another.

These changes are major enough that a fair number of existing Python
libraries that work directly with Python's internals (e.g., Cython)
would need to be rewritten. But the cadence of Python's release
schedule just means such breaking changes would need to be made in a
major point release instead of a minor one.

Related:

  * Python
  * Programming Languages
  * Software Development

Serdar Yegulalp is a senior writer at InfoWorld, focused on machine
learning, containerization, devops, the Python ecosystem, and
periodic reviews.

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