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PEP 556 -- Threaded garbage collection

Title:Threaded garbage collection
Author:Antoine Pitrou <solipsis at>
Type:Standards Track


This PEP proposes a new optional mode of operation for CPython's cyclic garbage collector (GC) where implicit (i.e. opportunistic) collections happen in a dedicated thread rather than synchronously.


An "implicit" GC run (or "implicit" collection) is one that is triggered opportunistically based on a certain heuristic computed over allocation statistics, whenever a new allocation is requested. Details of the heuristic are not relevant to this PEP, as it does not propose to change it.

An "explicit" GC run (or "explicit" collection) is one that is requested programmatically by an API call such as gc.collect.

"Threaded" refers to the fact that GC runs happen in a dedicated thread separate from sequential execution of application code. It does not mean "concurrent" (the Global Interpreter Lock, or GIL, still serializes execution among Python threads including the dedicated GC thread) nor "parallel" (the GC is not able to distribute its work onto several threads at once to lower wall-clock latencies of GC runs).


The mode of operation for the GC has always been to perform implicit collections synchronously. That is, whenever the aforementioned heuristic is activated, execution of application code in the current thread is suspended and the GC is launched in order to reclaim dead reference cycles.

There is a catch, though. Over the course of reclaiming dead reference cycles (and any ancillary objects hanging at those cycles), the GC can execute arbitrary finalization code in the form of __del__ methods and weakref callbacks. Over the years, Python has been used for more and more sophisticated purposes, and it is increasingly common for finalization code to perform complex tasks, for example in distributed systems where loss of an object may require notifying other (logical or physical) nodes.

Interrupting application code at arbitrary points to execute finalization code that may rely on a consistent internal state and/or on acquiring synchronization primitives give rise to reentrancy issues that even the most seasoned experts have trouble fixing properly [1].

This PEP bases itself on the observation that, despite the apparent similarities, same-thread reentrancy is a fundamentally harder problem than multi-thread synchronization. Instead of letting each developer or library author struggle with extremely hard reentrancy issues, one by one, this PEP proposes to allow the GC to run in a separate thread where well-known multi-thread synchronization practices are sufficient.


Under this PEP, the GC has two modes of operation:

  • "serial", which is the default and legacy mode, where an implicit GC run is performed immediately in the thread that detects such an implicit run is desired (based on the aforementioned allocation heuristic).
  • "threaded", which can be explicitly enabled at runtime on a per-process basis, where implicit GC runs are scheduled whenever the allocation heuristic is triggered, but run in a dedicated background thread.

Hard reentrancy problems which plague sophisticated uses of finalization callbacks in the "serial" mode become relatively easy multi-thread synchronization problems in the "threaded" mode of operation.

The GC also traditionally allows for explicit GC runs, using the Python API gc.collect and the C API PyGC_Collect. The visible semantics of these two APIs are left unchanged: they perform a GC run immediately when called, and only return when the GC run is finished.

New public APIs

Two new Python APIs are added to the gc module:

  • gc.set_mode(mode) sets the current mode of operation (either "serial" or "threaded"). If setting to "serial" and the current mode is "threaded", then the function also waits for the GC thread to end.
  • gc.get_mode() returns the current mode of operation.

It is allowed to switch back and forth between modes of operation.

Intended use

Given the per-process nature of the switch and its repercussions on semantics of all finalization callbacks, it is recommended that it is set at the beginning of an application's code (and/or in initializers for child processes e.g. when using multiprocessing). Library functions should probably not mess with this setting, just as they shouldn't call gc.enable or gc.disable, but there's nothing to prevent them from doing so.


This PEP does not address reentrancy issues with other kinds of asynchronous code execution (for example signal handlers registered with the signal module). The author believes that the overwhelming majority of painful reentrancy issues occur with finalizers. Most of the time, signal handlers are able to set a single flag and/or wake up a file descriptor for the main program to notice. As for those signal handlers which raise an exception, they have to execute in-thread.

This PEP also does not change the execution of finalization callbacks when they are called as part of regular reference counting, i.e. when releasing a visible reference drops an object's reference count to zero. Since such execution happens at deterministic points in code, it is usually not a problem.

Internal details

gc module

An internal flag gc_is_threaded is added, telling whether GC is serial or threaded.

An internal structure gc_mutex is added to avoid two GC runs at once:

static struct {
    PyThread_type_lock lock;  /* taken when collecting */
    PyThreadState *owner;  /* whichever thread is currently collecting
                              (NULL if no collection is taking place) */
} gc_mutex;

An internal structure gc_thread is added to handle synchronization with the GC thread:

static struct {
   PyThread_type_lock wakeup; /* acts as an event
                                 to wake up the GC thread */
   int collection_requested; /* non-zero if collection requested */
   PyThread_type_lock done; /* acts as an event signaling
                               the GC thread has exited */
} gc_thread;

threading module

Two private functions are added to the threading module:

  • threading._ensure_dummy_thread(name) creates and registers a Thread instance for the current thread with the given name, and returns it.
  • threading._remove_dummy_thread(thread) removes the given thread (as returned by _ensure_dummy_thread) from the threading module's internal state.

The purpose of these two functions is to improve debugging and introspection by letting threading.current_thread() return a more meaningfully-named object when called inside a finalization callback in the GC thread.


Here is a proposed pseudo-code for the main primitives, public and internal, required for implementing this PEP. All of them will be implemented in C and live inside the gc module, unless otherwise noted:

def collect_with_callback(generation):
    Collect up to the given *generation*.
    # Same code as currently (see collect_with_callback() in gcmodule.c)

def collect_generations():
    Collect as many generations as desired by the heuristic.
    # Same code as currently (see collect_generations() in gcmodule.c)

def lock_and_collect(generation=-1):
    Perform a collection with thread safety.
    me = PyThreadState_GET()
    if gc_mutex.owner == me:
        # reentrant GC collection request, bail out
    gc_mutex.owner = me
        if generation >= 0:
            return collect_with_callback(generation)
            return collect_generations()
        gc_mutex.owner = NULL

def schedule_gc_request():
    Ask the GC thread to run an implicit collection.
    assert gc_is_threaded == True
    # Note this is extremely fast if a collection is already requested
    if gc_thread.collection_requested == False:
        gc_thread.collection_requested = True

def is_implicit_gc_desired():
    Whether an implicit GC run is currently desired based on allocation
    stats.  Return a generation number, or -1 if none desired.
    # Same heuristic as currently (see _PyObject_GC_Alloc in gcmodule.c)

def PyGC_Malloc():
    Allocate a GC-enabled object.
    # Update allocation statistics (same code as currently, omitted for brievity)
    if is_implicit_gc_desired():
        if gc_is_threaded:
    # Go ahead with allocation (same code as currently, omitted for brievity)

def gc_thread(interp_state):
    Dedicated loop for threaded GC.
    # Init Python thread state (omitted, see t_bootstrap in _threadmodule.c)
    # Optional: init thread in Python threading module, for better introspection
    me = threading._ensure_dummy_thread(name="GC thread")

    while gc_is_threaded == True:
        if gc_thread.collection_requested != 0:
            gc_thread.collection_requested = 0

    # Signal we're exiting
    # Free Python thread state (omitted)

def gc.set_mode(mode):
    Set current GC mode.  This is a process-global setting.
    if mode == "threaded":
        if not gc_is_threaded == False:
            # Launch thread
            gc_thread.done.acquire(block=False)  # should not fail
            gc_is_threaded = True
    elif mode == "serial":
        if gc_is_threaded == True:
            # Wake up thread, asking it to end
            gc_is_threaded = False
            # Wait for thread exit
        raise ValueError("unsupported mode %r" % (mode,))

def gc.get_mode(mode):
    Get current GC mode.
    return "threaded" if gc_is_threaded else "serial"

def gc.collect(generation=2):
    Schedule collection of the given generation and wait for it to
    return lock_and_collect(generation)


Default mode

One may wonder whether the default mode should simply be changed to "threaded". For multi-threaded applications, it would probably not be a problem: those applications must already be prepared for finalization handlers to be run in arbitrary threads. In single-thread applications, however, it is currently guaranteed that finalizers will always be called in the main thread. Breaking this property may induce subtle behaviour changes or bugs, for example if finalizers rely on some thread-local values.

Another problem is when a program uses fork() for concurrency. Calling fork() from a single-threaded program is safe, but it's fragile (to say the least) if the program is multi-threaded.

Explicit collections

One may ask whether explicit collections should also be delegated to the background thread. The answer is it doesn't really matter: since gc.collect and PyGC_Collect actually wait for the collection to end (breaking this property would break compatibility), delegating the actual work to a background thread wouldn't ease synchronization with the thread requesting an explicit collection.

In the end, this PEP choses the behaviour that seems simpler to implement based on the pseudo-code above.

Impact on memory use

The "threaded" mode incurs a slight delay in implicit collections compared to the default "serial" mode. This obviously may change the memory profile of certain applications. By how much remains to be measured in real-world use, but we expect the impact to remain minor and bearable. First because implicit collections are based on a heuristic whose effect does not result in deterministic visible behaviour anyway. Second because the GC deals with reference cycles while many objects are reclaimed immediately when their last visible reference disappears.

Impact on CPU consumption

The pseudo-code above adds two lock operations for each implicit collection request in "threaded" mode: one in the thread making the request (a release call) and one in the GC thread (an acquire call). It also adds two other lock operations, regardless of the current mode, around each actual collection.

We expect the cost of those lock operations to be very small, on modern systems, compared to the actual cost of crawling through the chains of pointers during the collection itself ("pointer chasing" being one of the hardest workloads on modern CPUs, as it lends itself poorly to speculation and superscalar execution).

Actual measurements on worst-case mini-benchmarks may help provide reassuring upper bounds.

Impact on GC pauses

While this PEP does not concern itself with GC pauses, there is a practical chance that releasing the GIL at some point during an implicit collection (for example by virtue of executing a pure Python finalizer) will allow application code to run in-between, lowering the visible GC pause time for some applications.

If this PEP is accepted, future work may try to better realize this potential by speculatively releasing the GIL during collections, though it is unclear how doable that is.

Open issues

  • gc.set_mode should probably be protected against multiple concurrent invocations. Also, it should raise when called from inside a GC run (i.e. from a finalizer).
  • What happens at shutdown? Does the GC thread run until _PyGC_Fini() is called?


No actual implementation exists as of yet.