cpython/InternalDocs/qsbr.md

Quiescent-State Based Reclamation (QSBR)

Introduction

When implementing lock-free data structures, a key challenge is determining when it is safe to free memory that has been logically removed from a structure. Freeing memory too early can lead to use-after-free bugs if another thread is still accessing it. Freeing it too late results in excessive memory consumption.

Safe memory reclamation (SMR) schemes address this by delaying the free operation until all concurrent read accesses are guaranteed to have completed. Quiescent-State Based Reclamation (QSBR) is a SMR scheme used in Python's free-threaded build to manage the lifecycle of shared memory.

QSBR requires threads to periodically report that they are in a quiescent state. A thread is in a quiescent state if it holds no references to shared objects that might be reclaimed. Think of it as a checkpoint where a thread signals, "I am not in the middle of any operation that relies on a shared resource." In Python, the eval_breaker provides a natural and convenient place for threads to report this state.

Use in Free-Threaded Python

While CPython's memory management is dominated by reference counting and a tracing garbage collector, these mechanisms are not suitable for all data structures. For example, the backing array of a list object is not individually reference-counted but may have a shorter lifetime than the PyListObject that contains it. We could delay reclamation until the next GC run, but we want reclamation to be prompt and to run the GC less frequently in the free-threaded build, as it requires pausing all threads.

Many operations in the free-threaded build are protected by locks. However, for performance-critical code, we want to allow reads to happen concurrently with updates. For instance, we want to avoid locking during most list read accesses. If a list is resized while another thread is reading it, QSBR provides the mechanism to determine when it is safe to free the list's old backing array.

Specific use cases for QSBR include:

• Dictionary keys (PyDictKeysObject) and list arrays (_PyListArray): When a dictionary or list that may be shared between threads is resized, we use QSBR to delay freeing the old keys or array until it's safe. For dicts and lists that are not shared, their storage can be freed immediately upon resize.

• Mimalloc mi_page_t: Non-locking dictionary and list accesses require cooperation from the memory allocator. If an object is freed and its memory is reused, we must ensure the new object's reference count field is at the same memory location. In practice, this means when a mimalloc page (mi_page_t) becomes empty, we don't immediately allow it to be reused for allocations of a different size class. QSBR is used to determine when it's safe to repurpose the page or return its memory to the OS.

Implementation Details

Core Implementation

The proposal to add QSBR to Python is contained in Github issue 115103. Many details of that proposal have been copied here, so they can be kept up-to-date with the actual implementation.

Python's QSBR implementation is based on FreeBSD's "Global Unbounded Sequences." ¹²³. It relies on a few key counters:

• Global Write Sequence (wr_seq): A per-interpreter counter, wr_seq, is started at 1 and incremented by 2 each time it is advanced. This ensures its value is always odd, which can be used to distinguish it from other state values. When an object needs to be reclaimed, wr_seq is advanced, and the object is tagged with this new sequence number.

• Per-Thread Read Sequence: Each thread has a local read sequence counter. When a thread reaches a quiescent state (e.g., at the eval_breaker), it copies the current global wr_seq to its local counter.

• Global Read Sequence (rd_seq): This per-interpreter value stores the minimum of all per-thread read sequence counters (excluding detached threads). It is updated by a "polling" operation.

To free an object, the following steps are taken:

1. Advance the global wr_seq.

2. Add the object's pointer to a deferred-free list, tagging it with the new wr_seq value as its qsbr_goal.

Periodically, a polling mechanism processes this deferred-free list:

1. The minimum read sequence value across all active threads is calculated and stored as the global rd_seq.

2. For each item on the deferred-free list, if its qsbr_goal is less than or equal to the new rd_seq, its memory is freed, and it is removed from the: list. Otherwise, it remains on the list for a future attempt.

Deferred Advance Optimization

To reduce memory contention from frequent updates to the global wr_seq, its advancement is sometimes deferred. Instead of incrementing wr_seq on every reclamation request, each thread tracks its number of deferrals locally. Once the deferral count reaches a limit (QSBR_DEFERRED_LIMIT, currently 10), the thread advances the global wr_seq and resets its local count.

When an object is added to the deferred-free list, its qsbr_goal is set to wr_seq + 2. By setting the goal to the next sequence value, we ensure it's safe to defer the global counter advancement. This optimization improves runtime speed but may increase peak memory usage by slightly delaying when memory can be reclaimed.

Limitations

Determining the rd_seq requires scanning over all thread states. This operation could become a bottleneck in applications with a very large number of threads (e.g., >1,000). Future work may address this with more advanced mechanisms, such as a tree-based structure or incremental scanning. For now, the implementation prioritizes simplicity, with plans for refinement if multi-threaded benchmarks reveal performance issues.

References

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1. https://youtu.be/ZXUIFj4nRjk?t=694 ↩︎

2. https://people.kernel.org/joelfernandes/gus-vs-rcu ↩︎

3. http://bxr.su/FreeBSD/sys/kern/subr_smr.c#44 ↩︎