Python experimental support for free threading
**********************************************

Starting with the 3.13 release, CPython has experimental support for a
build of Python called *free threading* where the *global interpreter
lock* (GIL) is disabled.  Free-threaded execution allows for full
utilization of the available processing power by running threads in
parallel on available CPU cores. While not all software will benefit
from this automatically, programs designed with threading in mind will
run faster on multi-core hardware.

**The free-threaded mode is experimental** and work is ongoing to
improve it: expect some bugs and a substantial single-threaded
performance hit.

This document describes the implications of free threading for Python
code.  See C API Extension Support for Free Threading for information
on how to write C extensions that support the free-threaded build.

Ayrıca bakınız:

  **PEP 703** – Making the Global Interpreter Lock Optional in CPython
  for an overall description of free-threaded Python.


Installation
============

Starting with Python 3.13, the official macOS and Windows installers
optionally support installing free-threaded Python binaries.  The
installers are available at https://www.python.org/downloads/.

For information on other platforms, see the Installing a Free-Threaded
Python, a community-maintained installation guide for installing free-
threaded Python.

When building CPython from source, the "--disable-gil" configure
option should be used to build a free-threaded Python interpreter.


Identifying free-threaded Python
================================

To check if the current interpreter supports free-threading, "python
-VV" and "sys.version" contain "experimental free-threading build".
The new "sys._is_gil_enabled()" function can be used to check whether
the GIL is actually disabled in the running process.

The "sysconfig.get_config_var("Py_GIL_DISABLED")" configuration
variable can be used to determine whether the build supports free
threading.  If the variable is set to "1", then the build supports
free threading.  This is the recommended mechanism for decisions
related to the build configuration.


The global interpreter lock in free-threaded Python
===================================================

Free-threaded builds of CPython support optionally running with the
GIL enabled at runtime using the environment variable "PYTHON_GIL" or
the command-line option "-X gil".

The GIL may also automatically be enabled when importing a C-API
extension module that is not explicitly marked as supporting free
threading.  A warning will be printed in this case.

In addition to individual package documentation, the following
websites track the status of popular packages support for free
threading:

* https://py-free-threading.github.io/tracking/

* https://hugovk.github.io/free-threaded-wheels/


Thread safety
=============

The free-threaded build of CPython aims to provide similar thread-
safety behavior at the Python level to the default GIL-enabled build.
Built-in types like "dict", "list", and "set" use internal locks to
protect against concurrent modifications in ways that behave similarly
to the GIL.  However, Python has not historically guaranteed specific
behavior for concurrent modifications to these built-in types, so this
should be treated as a description of the current implementation, not
a guarantee of current or future behavior.

Not:

  It's recommended to use the "threading.Lock" or other
  synchronization primitives instead of relying on the internal locks
  of built-in types, when possible.


Known limitations
=================

This section describes known limitations of the free-threaded CPython
build.


Immortalization
---------------

The free-threaded build of the 3.13 release makes some objects
*immortal*. Immortal objects are not deallocated and have reference
counts that are never modified.  This is done to avoid reference count
contention that would prevent efficient multi-threaded scaling.

An object will be made immortal when a new thread is started for the
first time after the main thread is running.  The following objects
are immortalized:

* function objects declared at the module level

* method descriptors

* code objects

* *module* objects and their dictionaries

* classes (type objects)

Because immortal objects are never deallocated, applications that
create many objects of these types may see increased memory usage.
This is expected to be addressed in the 3.14 release.

Additionally, numeric and string literals in the code as well as
strings returned by "sys.intern()" are also immortalized.  This
behavior is expected to remain in the 3.14 free-threaded build.


Frame objects
-------------

It is not safe to access frame objects from other threads and doing so
may cause your program to crash .  This means that
"sys._current_frames()" is generally not safe to use in a free-
threaded build.  Functions like "inspect.currentframe()" and
"sys._getframe()" are generally safe as long as the resulting frame
object is not passed to another thread.


Iterators
---------

Sharing the same iterator object between multiple threads is generally
not safe and threads may see duplicate or missing elements when
iterating or crash the interpreter.


Single-threaded performance
---------------------------

The free-threaded build has additional overhead when executing Python
code compared to the default GIL-enabled build.  In 3.13, this
overhead is about 40% on the pyperformance suite. Programs that spend
most of their time in C extensions or I/O will see less of an impact.
The largest impact is because the specializing adaptive interpreter
(**PEP 659**) is disabled in the free-threaded build.  We expect to
re-enable it in a thread-safe way in the 3.14 release.  This overhead
is expected to be reduced in upcoming Python release.   We are aiming
for an overhead of 10% or less on the pyperformance suite compared to
the default GIL-enabled build.
