Python Enhancement Proposals

PEP 684 – A Per-Interpreter GIL

A Per-Interpreter GIL
Eric Snow <ericsnowcurrently at>
Standards Track



Since Python 1.5 (1997), CPython users can run multiple interpreters in the same process. However, interpreters in the same process have always shared a significant amount of global state. This is a source of bugs, with a growing impact as more and more people use the feature. Furthermore, sufficient isolation would facilitate true multi-core parallelism, where interpreters no longer share the GIL. The changes outlined in this proposal will result in that level of interpreter isolation.

High-Level Summary

At a high level, this proposal changes CPython in the following ways:

  • stops sharing the GIL between interpreters, given sufficient isolation
  • adds several new interpreter config options for isolation settings
  • adds some public C-API for fine-grained control when creating interpreters
  • keeps incompatible extensions from causing problems


The GIL protects concurrent access to most of CPython’s runtime state. So all that GIL-protected global state must move to each interpreter before the GIL can.

(In a handful of cases, other mechanisms can be used to ensure thread-safe sharing instead, such as locks or “immortal” objects.)

CPython Runtime State

Properly isolating interpreters requires that most of CPython’s runtime state be stored in the PyInterpreterState struct. Currently, only a portion of it is; the rest is found either in global variables or in _PyRuntimeState. Most of that will have to be moved.

This directly coincides with an ongoing effort (of many years) to greatly reduce internal use of C global variables and consolidate the runtime state into _PyRuntimeState and PyInterpreterState. (See Consolidating Runtime Global State below.) That project has significant merit on its own and has faced little controversy. So, while a per-interpreter GIL relies on the completion of that effort, that project should not be considered a part of this proposal–only a dependency.

Other Isolation Considerations

CPython’s interpreters must be strictly isolated from each other, with few exceptions. To a large extent they already are. Each interpreter has its own copy of all modules, classes, functions, and variables. The CPython C-API docs explain further.

However, aside from what has already been mentioned (e.g. the GIL), there are a couple of ways in which interpreters still share some state.

First of all, some process-global resources (e.g. memory, file descriptors, environment variables) are shared. There are no plans to change this.

Second, some isolation is faulty due to bugs or implementations that did not take multiple interpreters into account. This includes CPython’s runtime and the stdlib, as well as extension modules that rely on global variables. Bugs should be opened in these cases, as some already have been.

Depending on Immortal Objects

PEP 683 introduces immortal objects as a CPython-internal feature. With immortal objects, we can share any otherwise immutable global objects between all interpreters. Consequently, this PEP does not need to address how to deal with the various objects exposed in the public C-API. It also simplifies the question of what to do about the builtin static types. (See Global Objects below.)

Both issues have alternate solutions, but everything is simpler with immortal objects. If PEP 683 is not accepted then this one will be updated with the alternatives. This lets us reduce noise in this proposal.


The fundamental problem we’re solving here is a lack of true multi-core parallelism (for Python code) in the CPython runtime. The GIL is the cause. While it usually isn’t a problem in practice, at the very least it makes Python’s multi-core story murky, which makes the GIL a consistent distraction.

Isolated interpreters are also an effective mechanism to support certain concurrency models. PEP 554 discusses this in more detail.

Indirect Benefits

Most of the effort needed for a per-interpreter GIL has benefits that make those tasks worth doing anyway:

  • makes multiple-interpreter behavior more reliable
  • has led to fixes for long-standing runtime bugs that otherwise hadn’t been prioritized
  • has been exposing (and inspiring fixes for) previously unknown runtime bugs
  • has driven cleaner runtime initialization (PEP 432, PEP 587)
  • has driven cleaner and more complete runtime finalization
  • led to structural layering of the C-API (e.g. Include/internal)
  • also see Benefits to Consolidation below

Furthermore, much of that work benefits other CPython-related projects:

  • performance improvements (“faster-cpython”)
  • pre-fork application deployment (e.g. Instagram)
  • extension module isolation (see PEP 630, etc.)
  • embedding CPython

Existing Use of Multiple Interpreters

The C-API for multiple interpreters has been used for many years. However, until relatively recently the feature wasn’t widely known, nor extensively used (with the exception of mod_wsgi).

In the last few years use of multiple interpreters has been increasing. Here are some of the public projects using the feature currently:

Note that, with PEP 554, multiple interpreter usage would likely grow significantly (via Python code rather than the C-API).

PEP 554

PEP 554 is strictly about providing a minimal stdlib module to give users access to multiple interpreters from Python code. In fact, it specifically avoids proposing any changes related to the GIL. Consider, however, that users of that module would benefit from a per-interpreter GIL, which makes PEP 554 more appealing.


During initial investigations in 2014, a variety of possible solutions for multi-core Python were explored, but each had its drawbacks without simple solutions:

  • the existing practice of releasing the GIL in extension modules * doesn’t help with Python code
  • other Python implementations (e.g. Jython, IronPython) * CPython dominates the community
  • remove the GIL (e.g. gilectomy, “no-gil”) * too much technical risk (at the time)
  • Trent Nelson’s “PyParallel” project * incomplete; Windows-only at the time
  • multiprocessing
    • too much work to make it effective enough; high penalties in some situations (at large scale, Windows)
  • other parallelism tools (e.g. dask, ray, MPI) * not a fit for the stdlib
  • give up on multi-core (e.g. async, do nothing) * this can only end in tears

Even in 2014, it was fairly clear that a solution using isolated interpreters did not have a high level of technical risk and that most of the work was worth doing anyway. (The downside was the volume of work to be done.)


As summarized above, this proposal involves the following changes, in the order they must happen:

  1. consolidate global runtime state (including objects) into _PyRuntimeState
  2. move nearly all of the state down into PyInterpreterState
  3. finally, move the GIL down into PyInterpreterState
  4. everything else * add to the public C-API * implement restrictions in ExtensionFileLoader
    • work with popular extension maintainers to help with multi-interpreter support

Per-Interpreter State

The following runtime state will be moved to PyInterpreterState:

  • all global objects that are not safely shareable (fully immutable)
  • the GIL
  • mutable, currently protected by the GIL
  • mutable, currently protected by some other per-interpreter lock
  • mutable, may be used independently in different interpreters
  • all other mutable (or effectively mutable) state not otherwise excluded below

Furthermore, a number of parts of the global state have already been moved to the interpreter, such as GC, warnings, and atexit hooks.

The following state will not be moved:

  • global objects that are safely shareable, if any
  • immutable, often const
  • treated as immutable
  • related to CPython’s main() execution
  • related to the REPL
  • set during runtime init, then treated as immutable
  • mutable, protected by some global lock
  • mutable, atomic

Note that currently the allocators (see Objects/obmalloc.c) are shared between all interpreters, protected by the GIL. They will need to move to each interpreter (or a global lock will be needed). This is the highest risk part of the work to isolate interpreters and may require more than just moving fields down from _PyRuntimeState. Some of the complexity is reduced if CPython switches to a thread-safe allocator like mimalloc.


The following private API will be made public:

  • _PyInterpreterConfig
  • _Py_NewInterpreter() (as Py_NewInterpreterEx())

The following fields will be added to PyInterpreterConfig:

  • own_gil - (bool) create a new interpreter lock (instead of sharing with the main interpreter)
  • strict_extensions - fail import in this interpreter for incompatible extensions (see Restricting Extension Modules)

Restricting Extension Modules

Extension modules have many of the same problems as the runtime when state is stored in global variables. PEP 630 covers all the details of what extensions must do to support isolation, and thus safely run in multiple interpreters at once. This includes dealing with their globals.

Extension modules that do not implement isolation will only run in the main interpreter. In all other interpreters, the import will raise ImportError. This will be done through importlib._bootstrap_external.ExtensionFileLoader.

We will work with popular extensions to help them support use in multiple interpreters. This may involve adding to CPython’s public C-API, which we will address on a case-by-case basis.

Extension Module Compatibility

As noted in Extension Modules, many extensions work fine in multiple interpreters without needing any changes. The import system will still fail if such a module doesn’t explicitly indicate support. At first, not many extension modules will, so this is a potential source of frustration.

We will address this by adding a context manager to temporarily disable the check on multiple interpreter support: importlib.util.allow_all_extensions().


The “Sub-interpreter support” section of Doc/c-api/init.rst will be updated with the added API.


Backwards Compatibility

No behavior or APIs are intended to change due to this proposal, with one exception noted in the next section. The existing C-API for managing interpreters will preserve its current behavior, with new behavior exposed through new API. No other API or runtime behavior is meant to change, including compatibility with the stable ABI.

See Objects Exposed in the C-API below for related discussion.

Extension Modules

Currently the most common usage of Python, by far, is with the main interpreter running by itself. This proposal has zero impact on extension modules in that scenario. Likewise, for better or worse, there is no change in behavior under multiple interpreters created using the existing Py_NewInterpreter().

Keep in mind that some extensions already break when used in multiple interpreters, due to keeping module state in global variables. They may crash or, worse, experience inconsistent behavior. That was part of the motivation for PEP 630 and friends, so this is not a new situation nor a consequence of this proposal.

In contrast, when the proposed API is used to create multiple interpreters, the default behavior will change for some extensions. In that case, importing an extension will fail (outside the main interpreter) if it doesn’t indicate support for multiple interpreters. For extensions that already break in multiple interpreters, this will be an improvement.

Now we get to the break in compatibility mentioned above. Some extensions are safe under multiple interpreters, even though they haven’t indicated that. Unfortunately, there is no reliable way for the import system to infer that such an extension is safe, so importing them will still fail. This case is addressed in Extension Module Compatibility below.

Extension Module Maintainers

One related consideration is that a per-interpreter GIL will likely drive increased use of multiple interpreters, particularly if PEP 554 is accepted. Some maintainers of large extension modules have expressed concern about the increased burden they anticipate due to increased use of multiple interpreters.

Specifically, enabling support for multiple interpreters will require substantial work for some extension modules. To add that support, the maintainer(s) of such a module (often volunteers) would have to set aside their normal priorities and interests to focus on compatibility (see PEP 630).

Of course, extension maintainers are free to not add support for use in multiple interpreters. However, users will increasingly demand such support, especially if the feature grows in popularity.

Either way, the situation can be stressful for maintainers of such extensions, particularly when they are doing the work in their spare time. The concerns they have expressed are understandable, and we address the partial solution in Restricting Extension Modules below.

Alternate Python Implementations

Other Python implementation are not required to provide support for multiple interpreters in the same process (though some do already).

Security Implications

There is no known impact to security with this proposal.


On the one hand, this proposal has already motivated a number of improvements that make CPython more maintainable. That is expected to continue. On the other hand, the underlying work has already exposed various pre-existing defects in the runtime that have had to be fixed. That is also expected to continue as multiple interpreters receive more use. Otherwise, there shouldn’t be a significant impact on maintainability, so the net effect should be positive.


The work to consolidate globals has already provided a number of improvements to CPython’s performance, both speeding it up and using less memory, and this should continue. Performance benefits to a per-interpreter GIL have not been explored. At the very least, it is not expected to make CPython slower (as long as interpreters are sufficiently isolated).

How to Teach This

This is an advanced feature for users of the C-API. There is no expectation that this will be taught.

That said, if it were taught then it would boil down to the following:

In addition to Py_NewInterpreter(), you can use Py_NewInterpreterEx() to create an interpreter. The config you pass it indicates how you want that interpreter to behave.

Reference Implementation


Open Issues

  • What are the risks/hurdles involved with moving the allocators?
  • Is allow_all_extensions the best name for the context manager?

Deferred Functionality

  • PyInterpreterConfig option to always run the interpreter in a new thread
  • PyInterpreterConfig option to assign a “main” thread to the interpreter and only run in that thread

Rejected Ideas


Extra Context

Sharing Global Objects

We are sharing some global objects between interpreters. This is an implementation detail and relates more to globals consolidation than to this proposal, but it is a significant enough detail to explain here.

The alternative is to share no objects between interpreters, ever. To accomplish that, we’d have to sort out the fate of all our static types, as well as deal with compatibility issues for the many objects exposed in the public C-API.

That approach introduces a meaningful amount of extra complexity and higher risk, though prototyping has demonstrated valid solutions. Also, it would likely result in a performance penalty.

Immortal objects allow us to share the otherwise immutable global objects. That way we avoid the extra costs.

Objects Exposed in the C-API

The C-API (including the limited API) exposes all the builtin types, including the builtin exceptions, as well as the builtin singletons. The exceptions are exposed as PyObject * but the rest are exposed as the static values rather than pointers. This was one of the few non-trivial problems we had to solve for per-interpreter GIL.

With immortal objects this is a non-issue.

Consolidating Runtime Global State

As noted in CPython Runtime State above, there is an active effort (separate from this PEP) to consolidate CPython’s global state into the _PyRuntimeState struct. Nearly all the work involves moving that state from global variables. The project is particularly relevant to this proposal, so below is some extra detail.

Benefits to Consolidation

Consolidating the globals has a variety of benefits:

  • greatly reduces the number of C globals (best practice for C code)
  • the move draws attention to runtime state that is unstable or broken
  • encourages more consistency in how runtime state is used
  • makes multiple-interpreter behavior more reliable
  • leads to fixes for long-standing runtime bugs that otherwise haven’t been prioritized
  • exposes (and inspires fixes for) previously unknown runtime bugs
  • facilitates cleaner runtime initialization and finalization
  • makes it easier to discover/identify CPython’s runtime state
  • makes it easier to statically allocate runtime state in a consistent way
  • better memory locality for runtime state
  • structural layering of the C-API (e.g. Include/internal)

Furthermore, much of that work benefits other CPython-related projects:

  • performance improvements (“faster-cpython”)
  • pre-fork application deployment (e.g. Instagram)
  • extension module isolation (see PEP 630, etc.)
  • embedding CPython

Scale of Work

The number of global variables to be moved is large enough to matter, but most are Python objects that can be dealt with in large groups (like Py_IDENTIFIER). In nearly all cases, moving these globals to the interpreter is highly mechanical. That doesn’t require cleverness but instead requires someone to put in the time.

State To Be Moved

The remaining global variables can be categorized as follows:

  • global objects * static types (incl. exception types) * non-static types (incl. heap types, structseq types) * singletons (static) * singletons (initialized once) * cached objects
  • non-objects * will not (or unlikely to) change after init * only used in the main thread * initialized lazily * pre-allocated buffers * state

Those globals are spread between the core runtime, the builtin modules, and the stdlib extension modules.

For a breakdown of the remaining globals, run:

./python Tools/c-analyzer/ Tools/c-analyzer/cpython/globals-to-fix.tsv

Already Completed Work

As mentioned, this work has been going on for many years. Here are some of the things that have already been done:

  • cleanup of runtime initialization (see PEP 432 / PEP 587)
  • extension module isolation machinery (see PEP 384 / PEP 3121 / PEP 489)
  • isolation for many builtin modules
  • isolation for many stdlib extension modules
  • addition of _PyRuntimeState
  • no more _Py_IDENTIFIER()
  • statically allocated:
    • empty string
    • string literals
    • identifiers
    • latin-1 strings
    • length-1 bytes
    • empty tuple


As already indicated, there are several tools to help identify the globals and reason about them.

  • Tools/c-analyzer/cpython/globals-to-fix.tsv - the list of remaining globals
  • Tools/c-analyzer/ * analyze - identify all the globals * check - fail if there are any unsupported globals that aren’t ignored
  • Tools/c-analyzer/ - summarize the known globals

Also, the check for unsupported globals is incorporated into CI so that no new globals are accidentally added.

Global Objects

Global objects that are safe to be shared (without a GIL) between interpreters can stay on _PyRuntimeState. Not only must the object be effectively immutable (e.g. singletons, strings), but not even the refcount can change for it to be safe. Immortality (PEP 683) provides that. (The alternative is that no objects are shared, which adds significant complexity to the solution, particularly for the objects exposed in the public C-API.)

Builtin static types are a special case of global objects that will be shared. They are effectively immutable except for one part: __subclasses__ (AKA tp_subclasses). We expect that nothing else on a builtin type will change, even the content of __dict__ (AKA tp_dict).

__subclasses__ for the builtin types will be dealt with by making it a getter that does a lookup on the current PyInterpreterState for that type.




Last modified: 2022-03-09 16:08:07 GMT