All interned strings are immortal¶

For modern Python versions (since version 2.3), interning a string (e.g. with sys.intern()) does not cause it to become immortal. Instead, if the last reference to that string disappears, it will be removed from the interned string table. This is not the case for the free-threaded build and any interned string will become immortal, surviving until interpreter shutdown.

Non-GC objects have a larger object header¶

The free-threaded build uses a different PyObject structure. Instead of having the GC related information allocated before the PyObject structure, like in the default build, the GC related info is part of the normal object header. For example, on the AMD64 platform, None uses 32 bytes on the free-threaded build vs 16 bytes for the default build. GC objects (such as dicts and lists) are the same size for both builds since the free-threaded build does not use additional space for the GC info.

QSBR can delay freeing of memory¶

In order to safely implement lock-free data structures, a safe memory reclamation (SMR) scheme is used, known as quiescent state-based reclamation (QSBR). This means that the memory backing data structures allowing lock-free access will use QSBR, which defers the free operation, rather than immediately freeing the memory. Two examples of these data structures are the list object and the dictionary keys object. See InternalDocs/qsbr.md in the CPython source tree for more details on how QSBR is implemented. Running gc.collect() should cause all memory being held by QSBR to be actually freed. Note that even when QSBR frees the memory, the underlying memory allocator may not immediately return that memory to the OS and so the resident set size (RSS) of the process might not decrease.

mimalloc allocator vs pymalloc¶

The default build will normally use the “pymalloc” memory allocator for small allocations (512 bytes or smaller). The free-threaded build does not use pymalloc and allocates all Python objects using the “mimalloc” allocator. The pymalloc allocator has the following properties that help keep memory usage low: small per-allocated-block overhead, effective memory fragmentation prevention, and quick return of free memory to the operating system. The mimalloc allocator does quite well in these respects as well but can have some more overhead.

In the free-threaded build, mimalloc manages memory in a number of separate heaps (currently four). For example, all GC supporting objects are allocated from their own heap. Using separate heaps means that free memory in one heap cannot be used for an allocation that uses another heap. Also, some heaps are configured to use QSBR (quiescent-state based reclamation) when freeing the memory that backs up the heap (known as “pages” in mimalloc terminology). The use of QSBR creates a delay between all memory blocks for a page being freed and the memory page being released, either for new allocations or back to the OS.

The mimalloc allocator also defers returning freed memory back to the OS. You can reduce that delay by setting the environment variable MIMALLOC_PURGE_DELAY to 0. Note that this will likely reduce the performance of the allocator.

Free-threaded reference counting can cause objects to live longer¶

In the default build, when an object’s reference count reaches zero, it is normally deallocated. The free-threaded build uses “biased reference counting”, with a fast-path for objects “owned” by the current thread and a slow path for other objects. See PEP 703 for additional details. Any time an object’s reference count ends up in a “queued” state, deallocation can be deferred. The queued state is cleared from the “eval breaker” section of the bytecode evaluator.

The free-threaded build also allows a different mode of reference counting, known as “deferred reference counting”. This mode is enabled by setting a flag on a per-object basis. Deferred reference counting is enabled for the following types:

• module objects

• module top-level functions

• class methods defined in the class scope

• descriptor objects

• thread-local objects, created by threading.local

When deferred reference counting is enabled, references from Python function stacks are not added to the reference count. This scheme reduces the overhead of reference counting, especially for objects used from multiple threads. Because the stack references are not counted, objects with deferred reference counting are not immediately freed when their internal reference count goes to zero. Instead, they are examined by the next GC run and, if no stack references to them are found, they are freed. This means these objects are freed by the GC and not when their reference count goes to zero, as is typical.

Per-thread reference counting can delay freeing objects¶

To avoid contention on the reference count fields of frequently shared objects, the free-threaded build also uses “per-thread reference counting” for a few selected object types. Rather than updating a single shared reference count, each thread maintains its own local reference count array, indexed by a unique id assigned to the object. The true reference count is only computed by summing the per-thread counts when the object’s local count drops to zero. Per-thread reference counting is currently used for:

• heap type objects (classes created in Python)

• code objects

• the __dict__ of module objects

Because the per-thread counts must be merged back to the object before it can be deallocated, objects using per-thread reference counting are typically freed later than they would be in the default build. In particular, such an object is usually not freed until the thread that referenced it reaches a safe point (for example, in the “eval breaker” section of the bytecode evaluator) or exits. Running gc.collect() will merge the per-thread counts and allow these objects to be freed.