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<span id="id1"></span><h1>Thread Safety<a class="headerlink" href="#thread-safety" title="Link to this heading">#</a></h1>

<p>NumPy supports use in a multithreaded context via the <a class="reference external" href="https://docs.python.org/3/library/threading.html#module-threading" title="(in Python v3.14)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">threading</span></code></a> module in the

standard library. Many NumPy operations release the <a class="reference external" href="https://docs.python.org/3/glossary.html#term-GIL" title="(in Python v3.14)"><span>GIL</span></a>, so unlike many

situations in Python, it is possible to improve parallel performance by

exploiting multithreaded parallelism in Python.</p>

<p>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.</p>

<p>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.</p>

<p>In the future, we may add locking to <a class="reference internal" href="generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a> 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.</p>

<p>Note that operations that <em>do not</em> release the GIL will see no performance gains

from use of the <a class="reference external" href="https://docs.python.org/3/library/threading.html#module-threading" title="(in Python v3.14)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">threading</span></code></a> module, and instead might be better served with

<a class="reference external" href="https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing" title="(in Python v3.14)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">multiprocessing</span></code></a>. In particular, operations on arrays with <code class="docutils literal notranslate"><span class="pre">dtype=np.object_</span></code>

do not release the GIL.</p>

<h2>Context-local state<a class="headerlink" href="#context-local-state" title="Link to this heading">#</a></h2>

<p>NumPy maintains some state for ufuncs context-local basis, which means each

thread in a multithreaded program or task in an asyncio program has its own

independent configuration of the <a class="reference internal" href="generated/numpy.errstate.html#numpy.errstate" title="numpy.errstate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.errstate</span></code></a> (see

<a class="reference internal" href="routines.err.html"><span class="doc">Floating point error handling</span></a>), and of <a class="reference internal" href="routines.io.html#text-formatting-options"><span class="std std-ref">Text formatting options</span></a>.</p>

<p>You can update state stored in a context variable by entering a context manager.

As soon as the context manager exits, the state will be reset to its value

before entering the context manager.</p>

</section>

<h2>Free-threaded Python<a class="headerlink" href="#free-threaded-python" title="Link to this heading">#</a></h2>

<p><span class="versionmodified added">New in version 2.1.</span></p>

</div>

<p>Starting with NumPy 2.1 and CPython 3.13, NumPy also has experimental support

for python runtimes with the GIL disabled. See

<a class="reference external" href="https://py-free-threading.github.io">https://py-free-threading.github.io</a> for more information about installing and

using <a class="reference external" href="https://docs.python.org/3/glossary.html#term-free-threading" title="(in Python v3.14)"><span class="xref std std-term">free-threaded</span></a> Python, as well as

information about supporting it in libraries that depend on NumPy.</p>

<p>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

<a class="reference internal" href="generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a> object noted above, this also means that arrays with

<code class="docutils literal notranslate"><span class="pre">dtype=np.object_</span></code> are not protected by the GIL, creating data races for python

objects that are not possible outside free-threaded python.</p>

</section>

<h2>C-API Threading Support<a class="headerlink" href="#c-api-threading-support" title="Link to this heading">#</a></h2>

<p>For developers writing C extensions that interact with NumPy, several parts of

the <a class="reference internal" href="c-api/array.html"><span class="doc">C-API array documentation</span></a> provide detailed

information about multithreading considerations.</p>

</section>

<li><p><a class="reference internal" href="random/multithreading.html"><span class="doc">Multithreaded generation</span></a> - Practical example of using NumPy’s

random number generators in a multithreaded context with

<a class="reference external" href="https://docs.python.org/3/library/concurrent.futures.html#module-concurrent.futures" title="(in Python v3.14)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">concurrent.futures</span></code></a>.</p></li>

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