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# Thread Safety

NumPy supports use in a multithreaded context via the `threading` module in the standard library. Many NumPy operations release the GIL, so unlike many situations in Python, it is possible to improve parallel performance by exploiting multithreaded parallelism in Python.

The easiest performance gains happen when each worker thread owns its own array or set of array objects, with no data directly shared between threads. Because NumPy releases the GIL for many low-level operations, threads that spend most of the time in low-level code will run in parallel.

It is possible to share NumPy arrays between threads, but extreme care must be taken to avoid creating thread safety issues when mutating arrays that are shared between multiple threads. If two threads simultaneously read from and write to the same array, they will at best produce inconsistent, racey results that are not reproducible, let alone correct. It is also possible to crash the Python interpreter by, for example, resizing an array while another thread is reading from it to compute a ufunc operation.

In the future, we may add locking to ndarray to make writing multithreaded algorithms using NumPy arrays safer, but for now we suggest focusing on read-only access of arrays that are shared between threads, or adding your own locking if you need to mutation and multithreading.

Note that operations that do not release the GIL will see no performance gains from use of the `threading` module, and instead might be better served with `multiprocessing`. In particular, operations on arrays with `dtype=object` do not release the GIL.

## Free-threaded Python

Added in version 2.1.

Starting with NumPy 2.1 and CPython 3.13, NumPy also has experimental support for python runtimes with the GIL disabled. See https://py-free-threading.github.io for more information about installing and using free-threaded Python, as well as information about supporting it in libraries that depend on NumPy.

Because free-threaded Python does not have a global interpreter lock to serialize access to Python objects, there are more opportunities for threads to mutate shared state and create thread safety issues. In addition to the limitations about locking of the ndarray object noted above, this also means that arrays with `dtype=object` are not protected by the GIL, creating data races for python objects that are not possible outside free-threaded python.