<p dir="auto">The bug can occur in special cases e.g. when the divisor is scalar and equal to 9 or 13 and the dividend is array contains consecutive duplicate values of 233.</p>
<h3 dir="auto">Reproducing code example:</h3>
<div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="&gt;&gt;&gt; import numpy as np
&gt;&gt;&gt; a = np.full(64, 233, dtype=np.uint8)
&gt;&gt;&gt; a // 9
array([25, 26, 25, 26, 25, 26, 25, 26, 25, 26, 25, 26, 25, 26, 25, 26, 25,
       26, 25, 26, 25, 26, 25, 26, 25, 26, 25, 26, 25, 26, 25, 26, 25, 26,
       25, 26, 25, 26, 25, 26, 25, 26, 25, 26, 25, 26, 25, 26, 25, 26, 25,
       26, 25, 26, 25, 26, 25, 26, 25, 26, 25, 26, 25, 26], dtype=uint8)"><pre class="notranslate"><span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-k">import</span> <span class="pl-s1">numpy</span> <span class="pl-k">as</span> <span class="pl-s1">np</span>
<span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-s1">a</span> <span class="pl-c1">=</span> <span class="pl-s1">np</span>.<span class="pl-en">full</span>(<span class="pl-c1">64</span>, <span class="pl-c1">233</span>, <span class="pl-s1">dtype</span><span class="pl-c1">=</span><span class="pl-s1">np</span>.<span class="pl-s1">uint8</span>)
<span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-s1">a</span> <span class="pl-c1">//</span> <span class="pl-c1">9</span>
<span class="pl-en">array</span>([<span class="pl-c1">25</span>, <span class="pl-c1">26</span>, <span class="pl-c1">25</span>, <span class="pl-c1">26</span>, <span class="pl-c1">25</span>, <span class="pl-c1">26</span>, <span class="pl-c1">25</span>, <span class="pl-c1">26</span>, <span class="pl-c1">25</span>, <span class="pl-c1">26</span>, <span class="pl-c1">25</span>, <span class="pl-c1">26</span>, <span class="pl-c1">25</span>, <span class="pl-c1">26</span>, <span class="pl-c1">25</span>, <span class="pl-c1">26</span>, <span class="pl-c1">25</span>,
       <span class="pl-c1">26</span>, <span class="pl-c1">25</span>, <span class="pl-c1">26</span>, <span class="pl-c1">25</span>, <span class="pl-c1">26</span>, <span class="pl-c1">25</span>, <span class="pl-c1">26</span>, <span class="pl-c1">25</span>, <span class="pl-c1">26</span>, <span class="pl-c1">25</span>, <span class="pl-c1">26</span>, <span class="pl-c1">25</span>, <span class="pl-c1">26</span>, <span class="pl-c1">25</span>, <span class="pl-c1">26</span>, <span class="pl-c1">25</span>, <span class="pl-c1">26</span>,
       <span class="pl-c1">25</span>, <span class="pl-c1">26</span>, <span class="pl-c1">25</span>, <span class="pl-c1">26</span>, <span class="pl-c1">25</span>, <span class="pl-c1">26</span>, <span class="pl-c1">25</span>, <span class="pl-c1">26</span>, <span class="pl-c1">25</span>, <span class="pl-c1">26</span>, <span class="pl-c1">25</span>, <span class="pl-c1">26</span>, <span class="pl-c1">25</span>, <span class="pl-c1">26</span>, <span class="pl-c1">25</span>, <span class="pl-c1">26</span>, <span class="pl-c1">25</span>,
       <span class="pl-c1">26</span>, <span class="pl-c1">25</span>, <span class="pl-c1">26</span>, <span class="pl-c1">25</span>, <span class="pl-c1">26</span>, <span class="pl-c1">25</span>, <span class="pl-c1">26</span>, <span class="pl-c1">25</span>, <span class="pl-c1">26</span>, <span class="pl-c1">25</span>, <span class="pl-c1">26</span>, <span class="pl-c1">25</span>, <span class="pl-c1">26</span>], <span class="pl-s1">dtype</span><span class="pl-c1">=</span><span class="pl-s1">uint8</span>)</pre></div>
<h3 dir="auto">NumPy/Python version information:</h3>
<p dir="auto">main, after <a class="issue-link js-issue-link" data-error-text="Failed to load title" data-id="775015560" data-permission-text="Title is private" data-url="https://github.com/numpy/numpy/issues/18075" data-hovercard-type="pull_request" data-hovercard-url="/numpy/numpy/pull/18075/hovercard" href="https://github.com/numpy/numpy/pull/18075">#18075</a>. related to <a class="issue-link js-issue-link" data-error-text="Failed to load title" data-id="787552054" data-permission-text="Title is private" data-url="https://github.com/numpy/numpy/issues/18178" data-hovercard-type="pull_request" data-hovercard-url="/numpy/numpy/pull/18178/hovercard" href="https://github.com/numpy/numpy/pull/18178">#18178</a></p>
<p dir="auto">NumPy contains SIMD vectorized code for x86 SSE and AVX. This issue is a feature request to implement native, equivalent enablement for Power VSX, achieving equivalent speedup appropriate for the SIMD vector width of VSX (128 bits).</p>
<p dir="auto">EDIT (by <a class="user-mention notranslate" data-hovercard-type="user" data-hovercard-url="/users/rgommers/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" href="https://github.com/rgommers">@rgommers</a>): link to bounty: <a href="https://www.bountysource.com/issues/73221262-optimize-numpy-simd-algorithms-for-power-vsx" rel="nofollow">https://www.bountysource.com/issues/73221262-optimize-numpy-simd-algorithms-for-power-vsx</a></p>
<p dir="auto">The focus is PPC64LE Linux.  If the optimization can be portable to AIX (big endian) that's great, but not a strict requirement.  In other words, if AIX continues to use the scalar code for now, that's okay.</p>