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<section id="global-configuration-options">
<span id="global-state"></span><h1>Global Configuration Options<a class="headerlink" href="#global-configuration-options" title="Link to this heading">#</a></h1>
<p>NumPy has a few import-time, compile-time, or runtime configuration
options which change the global behaviour. Most of these are related to
performance or for debugging purposes and will not be interesting to the
vast majority of users.</p>
<section id="performance-related-options">
<h2>Performance-related options<a class="headerlink" href="#performance-related-options" title="Link to this heading">#</a></h2>
<section id="number-of-threads-used-for-linear-algebra">
<h3>Number of threads used for linear algebra<a class="headerlink" href="#number-of-threads-used-for-linear-algebra" title="Link to this heading">#</a></h3>
<p>NumPy itself is normally intentionally limited to a single thread
during function calls, however it does support multiple Python
threads running at the same time.
Note that for performant linear algebra NumPy uses a BLAS backend
such as OpenBLAS or MKL, which may use multiple threads that may
be controlled by environment variables such as <code class="docutils literal notranslate"><span class="pre">OMP_NUM_THREADS</span></code>
depending on what is used.
One way to control the number of threads is the package
<a class="reference external" href="https://pypi.org/project/threadpoolctl/">threadpoolctl</a></p>
</section>
<section id="madvise-hugepage-on-linux">
<h3>madvise hugepage on Linux<a class="headerlink" href="#madvise-hugepage-on-linux" title="Link to this heading">#</a></h3>
<p>When working with very large arrays on modern Linux kernels,
you can experience a significant speedup when
<a class="reference external" href="https://www.kernel.org/doc/html/latest/admin-guide/mm/transhuge.html">transparent hugepage</a>
is used.
The current system policy for transparent hugepages can be seen by:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cat</span> <span class="o">/</span><span class="n">sys</span><span class="o">/</span><span class="n">kernel</span><span class="o">/</span><span class="n">mm</span><span class="o">/</span><span class="n">transparent_hugepage</span><span class="o">/</span><span class="n">enabled</span>
</pre></div>
</div>
<p>When set to <code class="docutils literal notranslate"><span class="pre">madvise</span></code> NumPy will typically use hugepages for a performance
boost. This behaviour can be modified by setting the environment variable:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">NUMPY_MADVISE_HUGEPAGE</span><span class="o">=</span><span class="mi">0</span>
</pre></div>
</div>
<p>or setting it to <code class="docutils literal notranslate"><span class="pre">1</span></code> to always enable it. When not set, the default
is to use madvise on Kernels 4.6 and newer. These kernels presumably
experience a large speedup with hugepage support.
This flag is checked at import time.</p>
</section>
<section id="simd-feature-selection">
<h3>SIMD feature selection<a class="headerlink" href="#simd-feature-selection" title="Link to this heading">#</a></h3>
<p>Setting <code class="docutils literal notranslate"><span class="pre">NPY_DISABLE_CPU_FEATURES</span></code> will exclude simd features at runtime.
See <a class="reference internal" href="simd/build-options.html#runtime-simd-dispatch"><span class="std std-ref">Runtime dispatch</span></a>.</p>
</section>
</section>
<section id="debugging-related-options">
<h2>Debugging-related options<a class="headerlink" href="#debugging-related-options" title="Link to this heading">#</a></h2>
<section id="warn-if-no-memory-allocation-policy-when-deallocating-data">
<h3>Warn if no memory allocation policy when deallocating data<a class="headerlink" href="#warn-if-no-memory-allocation-policy-when-deallocating-data" title="Link to this heading">#</a></h3>
<p>Some users might pass ownership of the data pointer to the <code class="docutils literal notranslate"><span class="pre">ndarray</span></code> by
setting the <code class="docutils literal notranslate"><span class="pre">OWNDATA</span></code> flag. If they do this without setting (manually) a
memory allocation policy, the default will be to call <code class="docutils literal notranslate"><span class="pre">free</span></code>. If
<code class="docutils literal notranslate"><span class="pre">NUMPY_WARN_IF_NO_MEM_POLICY</span></code> is set to <code class="docutils literal notranslate"><span class="pre">"1"</span></code>, a <code class="docutils literal notranslate"><span class="pre">RuntimeWarning</span></code> will
be emitted. A better alternative is to use a <code class="docutils literal notranslate"><span class="pre">PyCapsule</span></code> with a deallocator
and set the <code class="docutils literal notranslate"><span class="pre">ndarray.base</span></code>.</p>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 1.25.2: </span>This variable is only checked on the first import.</p>
</div>
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