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<p class="MsoNormal">Hi Christopher,<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal" style="text-autospace:none">Yes, <span style="font-size:10.0pt;font-family:"Segoe UI",sans-serif;color:black">“is_partitioned” algo implementation is based on a reduction parallel pattern. <o:p></o:p></span></p>
<p class="MsoNormal" style="text-autospace:none"><span style="font-size:10.0pt;font-family:"Segoe UI",sans-serif;color:black">And it looks that a binary operation (combiner) is not commutative.<o:p></o:p></span></p>
<p class="MsoNormal" style="text-autospace:none"><span style="font-size:10.0pt;font-family:"Segoe UI",sans-serif;color:black"><o:p> </o:p></span></p>
<p class="MsoNormal" style="text-autospace:none"><span style="font-size:10.0pt;font-family:"Segoe UI",sans-serif;color:black">In general, “reduction” algorithm requires a commutative binary operation. And OpenMP reduction requires that.<o:p></o:p></span></p>
<p class="MsoNormal" style="text-autospace:none"><span style="font-size:10.0pt;font-family:"Segoe UI",sans-serif;color:black">For TBB backend it works because TBB parallel reduction algorithm doesn’t require a commutative binary operation.
<o:p></o:p></span></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">We (me or Evgeniy) will check that hypo and inform you.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Best regards,<o:p></o:p></p>
<p class="MsoNormal">Mikhail Dvorskiy<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><b>From:</b> Christopher Nelson <nadiasvertex@gmail.com> <br>
<b>Sent:</b> Thursday, October 1, 2020 2:46 AM<br>
<b>To:</b> Kukanov, Alexey <Alexey.Kukanov@intel.com><br>
<b>Cc:</b> Dvorskiy, Mikhail <mikhail.dvorskiy@intel.com>; Pavlov, Evgeniy <evgeniy.pavlov@intel.com>; Louis Dionne <ldionne@apple.com>; Thomas Rodgers <trodgers@redhat.com>; Libc++ Dev <libcxx-dev@lists.llvm.org><br>
<b>Subject:</b> [libcxx-dev] OpenMP parallel reduce bugs<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">Hello friends,<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">I have been working on the OpenMP backend for the parallel STL, and most of the tests are passing. However, among the failures is the "is_partitioned" test. I have rewritten the __parallel_reduce backend function to be simpler to understand
in an attempt to understand what is failing (code is below.)<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">I also rewrote it as a serial function that splits the iteration range in two and then calls __reduction() on each half of the range being passed in. The result I get from the serial execution as compared to the result I get from the parallel
execution is different.<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">I have verified that the parallel execution tasks are run, and that their results match what each serial execution would be if I ran them that way.<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">I am wondering if there is something wrong with the way OpenMP is running the reducer here? Perhaps it is injecting a value into the computation that is unexpected for this algorithm? Does anything jump out at anyone as being suspicious?<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">Thank you again for your time and assistance!<o:p></o:p></p>
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<pre><span style="font-size:11.5pt;color:#0033B3">template </span><span style="font-size:11.5pt;color:#080808"><</span><span style="font-size:11.5pt;color:#0033B3">class </span><span style="font-size:11.5pt;color:#371F80">_RandomAccessIterator</span><span style="font-size:11.5pt;color:#080808">, </span><span style="font-size:11.5pt;color:#0033B3">class </span><span style="font-size:11.5pt;color:#371F80">_Value</span><span style="font-size:11.5pt;color:#080808">, </span><span style="font-size:11.5pt;color:#0033B3">typename </span><span style="font-size:11.5pt;color:#371F80">_RealBody</span><span style="font-size:11.5pt;color:#080808">, </span><span style="font-size:11.5pt;color:#0033B3">typename </span><span style="font-size:11.5pt;color:#371F80">_Reduction</span><span style="font-size:11.5pt;color:#080808">><br></span><span style="font-size:11.5pt;color:#371F80">_Value<br></span><span style="font-size:11.5pt;color:#00627A">__parallel_reduce_body</span><span style="font-size:11.5pt;color:#080808">(</span><span style="font-size:11.5pt;color:#371F80">_RandomAccessIterator </span><span style="font-size:11.5pt;color:#080808">__first, </span><span style="font-size:11.5pt;color:#371F80">_RandomAccessIterator </span><span style="font-size:11.5pt;color:#080808">__last, </span><span style="font-size:11.5pt;color:#371F80">_Value </span><span style="font-size:11.5pt;color:#080808">__identity,<br> </span><span style="font-size:11.5pt;color:#371F80">_RealBody </span><span style="font-size:11.5pt;color:#080808">__real_body, </span><span style="font-size:11.5pt;color:#371F80">_Reduction </span><span style="font-size:11.5pt;color:#080808">__reduction)<br>{<br> std::size_t </span><span style="font-size:11.5pt;color:black">__item_count </span><span style="font-size:11.5pt;color:#080808">= __last - __first;<br> std::size_t </span><span style="font-size:11.5pt;color:black">__head_items </span><span style="font-size:11.5pt;color:#080808">= (__item_count / __default_chunk_size) * __default_chunk_size;<br><br> </span><i><span style="font-size:11.5pt;color:#8C8C8C">// We should encapsulate a result value and a reduction operator since we<br> // cannot use a lambda in OpenMP UDR.<br> </span></i><span style="font-size:11.5pt;color:#0033B3">using </span><span style="font-size:11.5pt;color:#371F80">_CombinerType </span><span style="font-size:11.5pt;color:#080808">= </span><span style="font-size:11.5pt;color:teal">__pstl</span><span style="font-size:11.5pt;color:#080808">::</span><span style="font-size:11.5pt;color:teal">__internal</span><span style="font-size:11.5pt;color:#080808">::</span><span style="font-size:11.5pt;color:teal">_Combiner</span><span style="font-size:11.5pt;color:#080808"><</span><span style="font-size:11.5pt;color:#371F80">_Value</span><span style="font-size:11.5pt;color:#080808">, </span><span style="font-size:11.5pt;color:#371F80">_Reduction</span><span style="font-size:11.5pt;color:#080808">>;<br> </span><span style="font-size:11.5pt;color:#371F80">_CombinerType </span><span style="font-size:11.5pt;color:black">__result</span><span style="font-size:11.5pt;color:#080808">{__identity, &__reduction};<br> _PSTL_PRAGMA_DECLARE_REDUCTION(__combiner, _CombinerType)<br><br> </span><i><span style="font-size:11.5pt;color:#8C8C8C">// To avoid over-subscription we use taskloop for the nested parallelism<br> //_PSTL_PRAGMA(omp taskloop reduction(__combiner : __result))<br> </span></i><span style="font-size:11.5pt;color:#0033B3">for </span><span style="font-size:11.5pt;color:#080808">(std::size_t __i = </span><span style="font-size:11.5pt;color:#1750EB">0</span><span style="font-size:11.5pt;color:#080808">; __i < __item_count; __i += __default_chunk_size)<br> {<br> </span><span style="font-size:11.5pt;color:#0033B3">auto </span><span style="font-size:11.5pt;color:#080808">__begin = __first + __i;<br> </span><span style="font-size:11.5pt;color:#0033B3">auto </span><span style="font-size:11.5pt;color:#080808">__end = __i < __head_items ? __begin + __default_chunk_size : __last;<br> __result.__value = __real_body(__begin, __end, __identity);<br> }<br><br> </span><span style="font-size:11.5pt;color:#0033B3">return </span><span style="font-size:11.5pt;color:black">__result</span><span style="font-size:11.5pt;color:#080808">.</span><span style="font-size:11.5pt;color:black">__value</span><span style="font-size:11.5pt;color:#080808">;<br>} <o:p></o:p></span></pre>
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