add references

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NunoSempere 2023-12-03 18:46:24 +00:00
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<h2 class="vector-pinnable-header-label">Contents</h2>
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<span class="vector-toc-numb">1</span>Algorithm</div>
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class="vector-toc-list-item vector-toc-level-1 vector-toc-list-item-expanded">
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<span class="vector-toc-numb">2</span>Time complexity</div>
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<span class="vector-toc-numb">3</span>Variants</div>
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class="vector-toc-list-item vector-toc-level-1 vector-toc-list-item-expanded">
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<span class="vector-toc-numb">4</span>See also</div>
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<span class="vector-toc-numb">5</span>References</div>
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<span class="vector-toc-numb">6</span>External links</div>
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<div id="mw-content-text" class="mw-body-content"><div class="mw-content-ltr mw-parser-output" lang="en" dir="ltr"><div class="shortdescription nomobile noexcerpt noprint searchaux" style="display:none">Algorithm for the kth smallest element in an array</div>
<style data-mw-deduplicate="TemplateStyles:r1097763485">.mw-parser-output .ambox{border:1px solid #a2a9b1;border-left:10px solid #36c;background-color:#fbfbfb;box-sizing:border-box}.mw-parser-output .ambox+link+.ambox,.mw-parser-output .ambox+link+style+.ambox,.mw-parser-output .ambox+link+link+.ambox,.mw-parser-output .ambox+.mw-empty-elt+link+.ambox,.mw-parser-output .ambox+.mw-empty-elt+link+style+.ambox,.mw-parser-output .ambox+.mw-empty-elt+link+link+.ambox{margin-top:-1px}html body.mediawiki .mw-parser-output .ambox.mbox-small-left{margin:4px 1em 4px 0;overflow:hidden;width:238px;border-collapse:collapse;font-size:88%;line-height:1.25em}.mw-parser-output .ambox-speedy{border-left:10px solid #b32424;background-color:#fee7e6}.mw-parser-output .ambox-delete{border-left:10px solid #b32424}.mw-parser-output .ambox-content{border-left:10px solid #f28500}.mw-parser-output .ambox-style{border-left:10px solid #fc3}.mw-parser-output .ambox-move{border-left:10px solid #9932cc}.mw-parser-output .ambox-protection{border-left:10px solid #a2a9b1}.mw-parser-output .ambox .mbox-text{border:none;padding:0.25em 0.5em;width:100%}.mw-parser-output .ambox .mbox-image{border:none;padding:2px 0 2px 0.5em;text-align:center}.mw-parser-output .ambox .mbox-imageright{border:none;padding:2px 0.5em 2px 0;text-align:center}.mw-parser-output .ambox .mbox-empty-cell{border:none;padding:0;width:1px}.mw-parser-output .ambox .mbox-image-div{width:52px}html.client-js body.skin-minerva .mw-parser-output .mbox-text-span{margin-left:23px!important}@media(min-width:720px){.mw-parser-output .ambox{margin:0 10%}}</style><table class="box-More_citations_needed plainlinks metadata ambox ambox-content ambox-Refimprove" role="presentation"><tbody><tr><td class="mbox-image"><div class="mbox-image-div"><span typeof="mw:File"><a href="/wiki/File:Question_book-new.svg" class="mw-file-description"><img alt="" src="//upload.wikimedia.org/wikipedia/en/thumb/9/99/Question_book-new.svg/50px-Question_book-new.svg.png" decoding="async" width="50" height="39" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/en/thumb/9/99/Question_book-new.svg/75px-Question_book-new.svg.png 1.5x, //upload.wikimedia.org/wikipedia/en/thumb/9/99/Question_book-new.svg/100px-Question_book-new.svg.png 2x" data-file-width="512" data-file-height="399" /></a></span></div></td><td class="mbox-text"><div class="mbox-text-span">This article <b>needs additional citations for <a href="/wiki/Wikipedia:Verifiability" title="Wikipedia:Verifiability">verification</a></b>.<span class="hide-when-compact"> Please help <a href="/wiki/Special:EditPage/Quickselect" title="Special:EditPage/Quickselect">improve this article</a> by <a href="/wiki/Help:Referencing_for_beginners" title="Help:Referencing for beginners">adding citations to reliable sources</a>. Unsourced material may be challenged and removed.<br /><small><span class="plainlinks"><i>Find sources:</i>&#160;<a rel="nofollow" class="external text" href="https://www.google.com/search?as_eq=wikipedia&amp;q=%22Quickselect%22">"Quickselect"</a>&#160;&#160;<a rel="nofollow" class="external text" href="https://www.google.com/search?tbm=nws&amp;q=%22Quickselect%22+-wikipedia&amp;tbs=ar:1">news</a>&#160;<b>·</b> <a rel="nofollow" class="external text" href="https://www.google.com/search?&amp;q=%22Quickselect%22&amp;tbs=bkt:s&amp;tbm=bks">newspapers</a>&#160;<b>·</b> <a rel="nofollow" class="external text" href="https://www.google.com/search?tbs=bks:1&amp;q=%22Quickselect%22+-wikipedia">books</a>&#160;<b>·</b> <a rel="nofollow" class="external text" href="https://scholar.google.com/scholar?q=%22Quickselect%22">scholar</a>&#160;<b>·</b> <a rel="nofollow" class="external text" href="https://www.jstor.org/action/doBasicSearch?Query=%22Quickselect%22&amp;acc=on&amp;wc=on">JSTOR</a></span></small></span> <span class="date-container"><i>(<span class="date">August 2013</span>)</i></span><span class="hide-when-compact"><i> (<small><a href="/wiki/Help:Maintenance_template_removal" title="Help:Maintenance template removal">Learn how and when to remove this template message</a></small>)</i></span></div></td></tr></tbody></table>
<style data-mw-deduplicate="TemplateStyles:r1066479718">.mw-parser-output .infobox-subbox{padding:0;border:none;margin:-3px;width:auto;min-width:100%;font-size:100%;clear:none;float:none;background-color:transparent}.mw-parser-output .infobox-3cols-child{margin:auto}.mw-parser-output .infobox .navbar{font-size:100%}body.skin-minerva .mw-parser-output .infobox-header,body.skin-minerva .mw-parser-output .infobox-subheader,body.skin-minerva .mw-parser-output .infobox-above,body.skin-minerva .mw-parser-output .infobox-title,body.skin-minerva .mw-parser-output .infobox-image,body.skin-minerva .mw-parser-output .infobox-full-data,body.skin-minerva .mw-parser-output .infobox-below{text-align:center}</style><table class="infobox"><caption class="infobox-title">Quickselect</caption><tbody><tr><td colspan="2" class="infobox-image"><span class="mw-default-size" typeof="mw:File"><a href="/wiki/File:Selecting_quickselect_frames.gif" class="mw-file-description" title="Animated visualization of the quickselect algorithm. Selecting the 22st smallest value."><img alt="Animated visualization of the quickselect algorithm. Selecting the 22st smallest value." src="//upload.wikimedia.org/wikipedia/commons/0/04/Selecting_quickselect_frames.gif" decoding="async" width="280" height="214" class="mw-file-element" data-file-width="280" data-file-height="214" /></a></span><div class="infobox-caption">Animated visualization of the quickselect algorithm. Selecting the 22nd smallest value.</div></td></tr><tr><th scope="row" class="infobox-label">Class</th><td class="infobox-data"><a href="/wiki/Selection_algorithm" title="Selection algorithm">Selection algorithm</a></td></tr><tr><th scope="row" class="infobox-label">Data structure</th><td class="infobox-data"><a href="/wiki/Array_data_structure" class="mw-redirect" title="Array data structure">Array</a></td></tr><tr><th scope="row" class="infobox-label"><a href="/wiki/Best,_worst_and_average_case" title="Best, worst and average case">Worst-case</a> <a href="/wiki/Time_complexity" title="Time complexity">performance</a></th><td class="infobox-data"><span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle O}">
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</math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/9d70e1d0d87e2ef1092ea1ffe2923d9933ff18fc" class="mwe-math-fallback-image-inline mw-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:1.773ex; height:2.176ex;" alt="O"></span>(<i>n</i><sup>2</sup>)</td></tr><tr><th scope="row" class="infobox-label"><a href="/wiki/Best,_worst_and_average_case" title="Best, worst and average case">Best-case</a> <a href="/wiki/Time_complexity" title="Time complexity">performance</a></th><td class="infobox-data"><span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle O}">
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</math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/9d70e1d0d87e2ef1092ea1ffe2923d9933ff18fc" class="mwe-math-fallback-image-inline mw-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:1.773ex; height:2.176ex;" alt="O"></span>(<i>n</i>)</td></tr><tr><th scope="row" class="infobox-label"><a href="/wiki/Best,_worst_and_average_case" title="Best, worst and average case">Average</a> <a href="/wiki/Time_complexity" title="Time complexity">performance</a></th><td class="infobox-data"><span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle O}">
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</math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/9d70e1d0d87e2ef1092ea1ffe2923d9933ff18fc" class="mwe-math-fallback-image-inline mw-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:1.773ex; height:2.176ex;" alt="O"></span>(<i>n</i>)</td></tr><tr><th scope="row" class="infobox-label">Optimal</th><td class="infobox-data">Yes</td></tr></tbody></table>
<p>In <a href="/wiki/Computer_science" title="Computer science">computer science</a>, <b>quickselect</b> is a <a href="/wiki/Selection_algorithm" title="Selection algorithm">selection algorithm</a> to find the <i>k</i>th smallest element in an unordered list, also known as the <i>k</i>th <a href="/wiki/Order_statistics" class="mw-redirect" title="Order statistics">order statistic</a>. Like the related <a href="/wiki/Quicksort" title="Quicksort">quicksort</a> sorting algorithm, it was developed by <a href="/wiki/Tony_Hoare" title="Tony Hoare">Tony Hoare</a>, and thus is also known as <b>Hoare's selection algorithm</b>.<sup id="cite_ref-1" class="reference"><a href="#cite_note-1">&#91;1&#93;</a></sup> Like quicksort, it is efficient in practice and has good average-case performance, but has poor worst-case performance. Quickselect and its variants are the selection algorithms most often used in efficient real-world implementations.
</p><p>Quickselect uses the same overall approach as quicksort, choosing one element as a pivot and partitioning the data in two based on the pivot, accordingly as less than or greater than the pivot. However, instead of recursing into both sides, as in quicksort, quickselect only recurses into one side the side with the element it is searching for. This reduces the average complexity from <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle O(n\log n)}">
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<annotation encoding="application/x-tex">{\displaystyle O(n\log n)}</annotation>
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</math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/9d2320768fb54880ca4356e61f60eb02a3f9d9f1" class="mwe-math-fallback-image-inline mw-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:10.118ex; height:2.843ex;" alt="O(n\log n)"></span> to <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle O(n)}">
<semantics>
<mrow class="MJX-TeXAtom-ORD">
<mstyle displaystyle="true" scriptlevel="0">
<mi>O</mi>
<mo stretchy="false">(</mo>
<mi>n</mi>
<mo stretchy="false">)</mo>
</mstyle>
</mrow>
<annotation encoding="application/x-tex">{\displaystyle O(n)}</annotation>
</semantics>
</math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/34109fe397fdcff370079185bfdb65826cb5565a" class="mwe-math-fallback-image-inline mw-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:4.977ex; height:2.843ex;" alt="O(n)"></span>, with a worst case of <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle O(n^{2})}">
<semantics>
<mrow class="MJX-TeXAtom-ORD">
<mstyle displaystyle="true" scriptlevel="0">
<mi>O</mi>
<mo stretchy="false">(</mo>
<msup>
<mi>n</mi>
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<mn>2</mn>
</mrow>
</msup>
<mo stretchy="false">)</mo>
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</mrow>
<annotation encoding="application/x-tex">{\displaystyle O(n^{2})}</annotation>
</semantics>
</math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/6cd9594a16cb898b8f2a2dff9227a385ec183392" class="mwe-math-fallback-image-inline mw-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:6.032ex; height:3.176ex;" alt="O(n^{2})"></span>.
</p><p>As with quicksort, quickselect is generally implemented as an <a href="/wiki/In-place_algorithm" title="In-place algorithm">in-place algorithm</a>, and beyond selecting the <span class="texhtml mvar" style="font-style:italic;">k</span>th element, it also partially sorts the data. See <a href="/wiki/Selection_algorithm" title="Selection algorithm">selection algorithm</a> for further discussion of the connection with sorting.
</p>
<meta property="mw:PageProp/toc" />
<h2><span class="mw-headline" id="Algorithm">Algorithm</span><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Quickselect&amp;action=edit&amp;section=1" title="Edit section: Algorithm"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></h2>
<p>In quicksort, there is a subprocedure called <code>partition</code> that can, in linear time, group a list (ranging from indices <code>left</code> to <code>right</code>) into two parts: those less than a certain element, and those greater than or equal to the element. Here is pseudocode that performs a partition about the element <code>list[pivotIndex]</code>:
</p>
<pre><b>function</b> partition(list, left, right, pivotIndex) <b>is</b>
pivotValue&#160;:= list[pivotIndex]
swap list[pivotIndex] and list[right] <i>// Move pivot to end</i>
storeIndex&#160;:= left
<b>for</b> i <b>from</b> left <b>to</b> right 1 <b>do</b>
<b>if</b> list[i] &lt; pivotValue <b>then</b>
swap list[storeIndex] and list[i]
increment storeIndex
swap list[right] and list[storeIndex] <i>// Move pivot to its final place</i>
<b>return</b> storeIndex
</pre>
<p>This is known as the <a href="/wiki/Quicksort#Lomuto_partition_scheme" title="Quicksort">Lomuto partition scheme</a>, which is simpler but less efficient than <a href="/wiki/Quicksort#Hoare_partition_scheme" title="Quicksort">Hoare's original partition scheme</a>.
</p><p>In quicksort, we recursively sort both branches, leading to best-case <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle O(n\log n)}">
<semantics>
<mrow class="MJX-TeXAtom-ORD">
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<mi>O</mi>
<mo stretchy="false">(</mo>
<mi>n</mi>
<mi>log</mi>
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<mi>n</mi>
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<annotation encoding="application/x-tex">{\displaystyle O(n\log n)}</annotation>
</semantics>
</math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/9d2320768fb54880ca4356e61f60eb02a3f9d9f1" class="mwe-math-fallback-image-inline mw-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:10.118ex; height:2.843ex;" alt="O(n\log n)"></span> time. However, when doing selection, we already know which partition our desired element lies in, since the pivot is in its final sorted position, with all those preceding it in an unsorted order and all those following it in an unsorted order. Therefore, a single recursive call locates the desired element in the correct partition, and we build upon this for quickselect:
</p>
<pre><i>// Returns the k-th smallest element of list within left..right inclusive</i>
<i>// (i.e. left &lt;= k &lt;= right).</i>
<b>function</b> select(list, left, right, k) <b>is</b>
<b>if</b> left = right <b>then</b> <i>// If the list contains only one element,</i>
<b>return</b> list[left] <i>// return that element</i>
pivotIndex &#160;:= ... <i>// select a pivotIndex between left and right,</i>
<i>// e.g.,</i> left + floor(rand()&#160;% (right left + 1))
pivotIndex &#160;:= partition(list, left, right, pivotIndex)
<i>// The pivot is in its final sorted position</i>
<b>if</b> k = pivotIndex <b>then</b>
<b>return</b> list[k]
<b>else if</b> k &lt; pivotIndex <b>then</b>
<b>return</b> select(list, left, pivotIndex 1, k)
<b>else</b>
<b>return</b> select(list, pivotIndex + 1, right, k)
</pre>
<hr /><p>Note the resemblance to quicksort: just as the minimum-based selection algorithm is a partial selection sort, this is a partial quicksort, generating and partitioning only <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle O(\log n)}">
<semantics>
<mrow class="MJX-TeXAtom-ORD">
<mstyle displaystyle="true" scriptlevel="0">
<mi>O</mi>
<mo stretchy="false">(</mo>
<mi>log</mi>
<mo>&#x2061;<!-- --></mo>
<mi>n</mi>
<mo stretchy="false">)</mo>
</mstyle>
</mrow>
<annotation encoding="application/x-tex">{\displaystyle O(\log n)}</annotation>
</semantics>
</math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/aae0f22048ba6b7c05dbae17b056bfa16e21807d" class="mwe-math-fallback-image-inline mw-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:8.336ex; height:2.843ex;" alt="O(\log n)"></span> of its <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle O(n)}">
<semantics>
<mrow class="MJX-TeXAtom-ORD">
<mstyle displaystyle="true" scriptlevel="0">
<mi>O</mi>
<mo stretchy="false">(</mo>
<mi>n</mi>
<mo stretchy="false">)</mo>
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<annotation encoding="application/x-tex">{\displaystyle O(n)}</annotation>
</semantics>
</math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/34109fe397fdcff370079185bfdb65826cb5565a" class="mwe-math-fallback-image-inline mw-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:4.977ex; height:2.843ex;" alt="O(n)"></span> partitions. This simple procedure has expected linear performance, and, like quicksort, has quite good performance in practice. It is also an <a href="/wiki/In-place_algorithm" title="In-place algorithm">in-place algorithm</a>, requiring only constant memory overhead if <a href="/wiki/Tail_call" title="Tail call">tail call</a> optimization is available, or if eliminating the <a href="/wiki/Tail_recursion" class="mw-redirect" title="Tail recursion">tail recursion</a> with a loop:
</p><pre><b>function</b> select(list, left, right, k) <b>is</b>
<b>loop</b>
<b>if</b> left = right <b>then</b>
<b>return</b> list[left]
pivotIndex&#160;:= ... <i>// select pivotIndex between left and right</i>
pivotIndex&#160;:= partition(list, left, right, pivotIndex)
<b>if</b> k = pivotIndex <b>then</b>
<b>return</b> list[k]
<b>else if</b> k &lt; pivotIndex <b>then</b>
right&#160;:= pivotIndex 1
<b>else</b>
left&#160;:= pivotIndex + 1
</pre>
<h2><span class="mw-headline" id="Time_complexity">Time complexity</span><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Quickselect&amp;action=edit&amp;section=2" title="Edit section: Time complexity"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></h2>
<p>Like quicksort, quickselect has good average performance, but is sensitive to the pivot that is chosen. If good pivots are chosen, meaning ones that consistently decrease the search set by a given fraction, then the search set decreases in size exponentially and by induction (or summing the <a href="/wiki/Geometric_series" title="Geometric series">geometric series</a>) one sees that performance is linear, as each step is linear and the overall time is a constant times this (depending on how quickly the search set reduces). However, if bad pivots are consistently chosen, such as decreasing by only a single element each time, then worst-case performance is quadratic: <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle O(n^{2}).}">
<semantics>
<mrow class="MJX-TeXAtom-ORD">
<mstyle displaystyle="true" scriptlevel="0">
<mi>O</mi>
<mo stretchy="false">(</mo>
<msup>
<mi>n</mi>
<mrow class="MJX-TeXAtom-ORD">
<mn>2</mn>
</mrow>
</msup>
<mo stretchy="false">)</mo>
<mo>.</mo>
</mstyle>
</mrow>
<annotation encoding="application/x-tex">{\displaystyle O(n^{2}).}</annotation>
</semantics>
</math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/e6e16485faf5f5237ac4fdf56e76ac7515282114" class="mwe-math-fallback-image-inline mw-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:6.678ex; height:3.176ex;" alt="{\displaystyle O(n^{2}).}"></span> This occurs for example in searching for the maximum element of a set, using the first element as the pivot, and having sorted data. However, for randomly chosen pivots, this worst case is very unlikely: the probability of using more than <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle Cn}">
<semantics>
<mrow class="MJX-TeXAtom-ORD">
<mstyle displaystyle="true" scriptlevel="0">
<mi>C</mi>
<mi>n</mi>
</mstyle>
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<annotation encoding="application/x-tex">{\displaystyle Cn}</annotation>
</semantics>
</math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/7eb08676206844b06ee1ddd827013317bd52e950" class="mwe-math-fallback-image-inline mw-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:3.161ex; height:2.176ex;" alt="{\displaystyle Cn}"></span> comparisons, for any sufficiently large constant <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle C}">
<semantics>
<mrow class="MJX-TeXAtom-ORD">
<mstyle displaystyle="true" scriptlevel="0">
<mi>C</mi>
</mstyle>
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<annotation encoding="application/x-tex">{\displaystyle C}</annotation>
</semantics>
</math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/4fc55753007cd3c18576f7933f6f089196732029" class="mwe-math-fallback-image-inline mw-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:1.766ex; height:2.176ex;" alt="C"></span>, is superexponentially small as a function of <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle C}">
<semantics>
<mrow class="MJX-TeXAtom-ORD">
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<mi>C</mi>
</mstyle>
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<annotation encoding="application/x-tex">{\displaystyle C}</annotation>
</semantics>
</math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/4fc55753007cd3c18576f7933f6f089196732029" class="mwe-math-fallback-image-inline mw-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:1.766ex; height:2.176ex;" alt="C"></span>.<sup id="cite_ref-2" class="reference"><a href="#cite_note-2">&#91;2&#93;</a></sup>
</p>
<h2><span class="mw-headline" id="Variants">Variants</span><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Quickselect&amp;action=edit&amp;section=3" title="Edit section: Variants"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></h2>
<p>The easiest solution is to choose a random pivot, which yields <a href="/wiki/Almost_certain" class="mw-redirect" title="Almost certain">almost certain</a> linear time. Deterministically, one can use median-of-3 pivot strategy (as in the quicksort), which yields linear performance on partially sorted data, as is common in the real world. However, contrived sequences can still cause worst-case complexity; <a href="/wiki/David_Musser" title="David Musser">David Musser</a> describes a "median-of-3 killer" sequence that allows an attack against that strategy, which was one motivation for his <a href="/wiki/Introselect" title="Introselect">introselect</a> algorithm.
</p><p>One can assure linear performance even in the worst case by using a more sophisticated pivot strategy; this is done in the <a href="/wiki/Median_of_medians" title="Median of medians">median of medians</a> algorithm. However, the overhead of computing the pivot is high, and thus this is generally not used in practice. One can combine basic quickselect with median of medians as fallback to get both fast average case performance and linear worst-case performance; this is done in <a href="/wiki/Introselect" title="Introselect">introselect</a>.
</p><p>Finer computations of the average time complexity yield a worst case of <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle n(2+2\log 2+o(1))\leq 3.4n+o(n)}">
<semantics>
<mrow class="MJX-TeXAtom-ORD">
<mstyle displaystyle="true" scriptlevel="0">
<mi>n</mi>
<mo stretchy="false">(</mo>
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<mn>2</mn>
<mi>log</mi>
<mo>&#x2061;<!-- --></mo>
<mn>2</mn>
<mo>+</mo>
<mi>o</mi>
<mo stretchy="false">(</mo>
<mn>1</mn>
<mo stretchy="false">)</mo>
<mo stretchy="false">)</mo>
<mo>&#x2264;<!-- ≤ --></mo>
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<mi>n</mi>
<mo>+</mo>
<mi>o</mi>
<mo stretchy="false">(</mo>
<mi>n</mi>
<mo stretchy="false">)</mo>
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<annotation encoding="application/x-tex">{\displaystyle n(2+2\log 2+o(1))\leq 3.4n+o(n)}</annotation>
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</math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/a08267fadce2d19b80e2d2fe4ef8a3a541c1216a" class="mwe-math-fallback-image-inline mw-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:34.854ex; height:2.843ex;" alt="n(2+2\log 2+o(1))\leq 3.4n+o(n)"></span> for random pivots (in the case of the median; other <i>k</i> are faster).<sup id="cite_ref-3" class="reference"><a href="#cite_note-3">&#91;3&#93;</a></sup> The constant can be improved to 3/2 by a more complicated pivot strategy, yielding the <a href="/wiki/Floyd%E2%80%93Rivest_algorithm" title="FloydRivest algorithm">FloydRivest algorithm</a>, which has average complexity of <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle 1.5n+O(n^{1/2})}">
<semantics>
<mrow class="MJX-TeXAtom-ORD">
<mstyle displaystyle="true" scriptlevel="0">
<mn>1.5</mn>
<mi>n</mi>
<mo>+</mo>
<mi>O</mi>
<mo stretchy="false">(</mo>
<msup>
<mi>n</mi>
<mrow class="MJX-TeXAtom-ORD">
<mn>1</mn>
<mrow class="MJX-TeXAtom-ORD">
<mo>/</mo>
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<mn>2</mn>
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</msup>
<mo stretchy="false">)</mo>
</mstyle>
</mrow>
<annotation encoding="application/x-tex">{\displaystyle 1.5n+O(n^{1/2})}</annotation>
</semantics>
</math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/45eddb6b735b6f51499d879f5b00fcfe40e0c484" class="mwe-math-fallback-image-inline mw-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:14.882ex; height:3.343ex;" alt="1.5n+O(n^{1/2})"></span> for median, with other <i>k</i> being faster.
</p>
<h2><span class="mw-headline" id="See_also">See also</span><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Quickselect&amp;action=edit&amp;section=4" title="Edit section: See also"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></h2>
<ul><li><a href="/wiki/Floyd%E2%80%93Rivest_algorithm" title="FloydRivest algorithm">FloydRivest algorithm</a></li>
<li><a href="/wiki/Introselect" title="Introselect">Introselect</a></li>
<li><a href="/wiki/Median_of_medians" title="Median of medians">Median of medians</a></li></ul>
<h2><span class="mw-headline" id="References">References</span><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Quickselect&amp;action=edit&amp;section=5" title="Edit section: References"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></h2>
<style data-mw-deduplicate="TemplateStyles:r1011085734">.mw-parser-output .reflist{font-size:90%;margin-bottom:0.5em;list-style-type:decimal}.mw-parser-output .reflist .references{font-size:100%;margin-bottom:0;list-style-type:inherit}.mw-parser-output .reflist-columns-2{column-width:30em}.mw-parser-output .reflist-columns-3{column-width:25em}.mw-parser-output .reflist-columns{margin-top:0.3em}.mw-parser-output .reflist-columns ol{margin-top:0}.mw-parser-output .reflist-columns li{page-break-inside:avoid;break-inside:avoid-column}.mw-parser-output .reflist-upper-alpha{list-style-type:upper-alpha}.mw-parser-output .reflist-upper-roman{list-style-type:upper-roman}.mw-parser-output .reflist-lower-alpha{list-style-type:lower-alpha}.mw-parser-output .reflist-lower-greek{list-style-type:lower-greek}.mw-parser-output .reflist-lower-roman{list-style-type:lower-roman}</style><div class="reflist">
<div class="mw-references-wrap"><ol class="references">
<li id="cite_note-1"><span class="mw-cite-backlink"><b><a href="#cite_ref-1">^</a></b></span> <span class="reference-text"><style data-mw-deduplicate="TemplateStyles:r1133582631">.mw-parser-output cite.citation{font-style:inherit;word-wrap:break-word}.mw-parser-output .citation q{quotes:"\"""\"""'""'"}.mw-parser-output .citation:target{background-color:rgba(0,127,255,0.133)}.mw-parser-output .id-lock-free a,.mw-parser-output .citation .cs1-lock-free a{background:url("//upload.wikimedia.org/wikipedia/commons/6/65/Lock-green.svg")right 0.1em center/9px no-repeat}.mw-parser-output .id-lock-limited a,.mw-parser-output .id-lock-registration a,.mw-parser-output .citation .cs1-lock-limited a,.mw-parser-output .citation .cs1-lock-registration a{background:url("//upload.wikimedia.org/wikipedia/commons/d/d6/Lock-gray-alt-2.svg")right 0.1em center/9px no-repeat}.mw-parser-output .id-lock-subscription a,.mw-parser-output .citation .cs1-lock-subscription a{background:url("//upload.wikimedia.org/wikipedia/commons/a/aa/Lock-red-alt-2.svg")right 0.1em center/9px no-repeat}.mw-parser-output .cs1-ws-icon a{background:url("//upload.wikimedia.org/wikipedia/commons/4/4c/Wikisource-logo.svg")right 0.1em center/12px no-repeat}.mw-parser-output .cs1-code{color:inherit;background:inherit;border:none;padding:inherit}.mw-parser-output .cs1-hidden-error{display:none;color:#d33}.mw-parser-output .cs1-visible-error{color:#d33}.mw-parser-output .cs1-maint{display:none;color:#3a3;margin-left:0.3em}.mw-parser-output .cs1-format{font-size:95%}.mw-parser-output .cs1-kern-left{padding-left:0.2em}.mw-parser-output .cs1-kern-right{padding-right:0.2em}.mw-parser-output .citation .mw-selflink{font-weight:inherit}</style><cite id="CITEREFHoare1961" class="citation journal cs1"><a href="/wiki/Tony_Hoare" title="Tony Hoare">Hoare, C. A. R.</a> (1961). "Algorithm 65: Find". <i><a href="/wiki/Communications_of_the_ACM" title="Communications of the ACM">Comm. ACM</a></i>. <b>4</b> (7): 321322. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1145%2F366622.366647">10.1145/366622.366647</a>.</cite><span title="ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.jtitle=Comm.+ACM&amp;rft.atitle=Algorithm+65%3A+Find&amp;rft.volume=4&amp;rft.issue=7&amp;rft.pages=321-322&amp;rft.date=1961&amp;rft_id=info%3Adoi%2F10.1145%2F366622.366647&amp;rft.aulast=Hoare&amp;rft.aufirst=C.+A.+R.&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AQuickselect" class="Z3988"></span></span>
</li>
<li id="cite_note-2"><span class="mw-cite-backlink"><b><a href="#cite_ref-2">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1133582631"><cite id="CITEREFDevroye1984" class="citation journal cs1">Devroye, Luc (1984). <a rel="nofollow" class="external text" href="http://luc.devroye.org/devroye-selection1984.pdf">"Exponential bounds for the running time of a selection algorithm"</a> <span class="cs1-format">(PDF)</span>. <i>Journal of Computer and System Sciences</i>. <b>29</b> (1): 17. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2F0022-0000%2884%2990009-6">10.1016/0022-0000(84)90009-6</a>. <a href="/wiki/MR_(identifier)" class="mw-redirect" title="MR (identifier)">MR</a>&#160;<a rel="nofollow" class="external text" href="https://mathscinet.ams.org/mathscinet-getitem?mr=0761047">0761047</a>.</cite><span title="ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.jtitle=Journal+of+Computer+and+System+Sciences&amp;rft.atitle=Exponential+bounds+for+the+running+time+of+a+selection+algorithm&amp;rft.volume=29&amp;rft.issue=1&amp;rft.pages=1-7&amp;rft.date=1984&amp;rft_id=info%3Adoi%2F10.1016%2F0022-0000%2884%2990009-6&amp;rft_id=https%3A%2F%2Fmathscinet.ams.org%2Fmathscinet-getitem%3Fmr%3D761047%23id-name%3DMR&amp;rft.aulast=Devroye&amp;rft.aufirst=Luc&amp;rft_id=http%3A%2F%2Fluc.devroye.org%2Fdevroye-selection1984.pdf&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AQuickselect" class="Z3988"></span> <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1133582631"><cite id="CITEREFDevroye2001" class="citation journal cs1">Devroye, Luc (2001). <a rel="nofollow" class="external text" href="https://luc.devroye.org/wcfind.pdf">"On the probabilistic worst-case time of 'find'<span class="cs1-kern-right"></span>"</a> <span class="cs1-format">(PDF)</span>. <i>Algorithmica</i>. <b>31</b> (3): 291303. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1007%2Fs00453-001-0046-2">10.1007/s00453-001-0046-2</a>. <a href="/wiki/MR_(identifier)" class="mw-redirect" title="MR (identifier)">MR</a>&#160;<a rel="nofollow" class="external text" href="https://mathscinet.ams.org/mathscinet-getitem?mr=1855252">1855252</a>.</cite><span title="ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.jtitle=Algorithmica&amp;rft.atitle=On+the+probabilistic+worst-case+time+of+%27find%27&amp;rft.volume=31&amp;rft.issue=3&amp;rft.pages=291-303&amp;rft.date=2001&amp;rft_id=info%3Adoi%2F10.1007%2Fs00453-001-0046-2&amp;rft_id=https%3A%2F%2Fmathscinet.ams.org%2Fmathscinet-getitem%3Fmr%3D1855252%23id-name%3DMR&amp;rft.aulast=Devroye&amp;rft.aufirst=Luc&amp;rft_id=https%3A%2F%2Fluc.devroye.org%2Fwcfind.pdf&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AQuickselect" class="Z3988"></span></span>
</li>
<li id="cite_note-3"><span class="mw-cite-backlink"><b><a href="#cite_ref-3">^</a></b></span> <span class="reference-text"><a rel="nofollow" class="external text" href="https://11011110.github.io/blog/2007/10/09/blum-style-analysis-of.html">Blum-style analysis of Quickselect</a>, <a href="/wiki/David_Eppstein" title="David Eppstein">David Eppstein</a>, October 9, 2007.</span>
</li>
</ol></div></div>
<h2><span class="mw-headline" id="External_links">External links</span><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Quickselect&amp;action=edit&amp;section=6" title="Edit section: External links"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></h2>
<ul><li>"<a rel="nofollow" class="external text" href="https://www.mathworks.com/matlabcentral/fileexchange/68947-qselect">qselect</a>", <i>Quickselect algorithm in Matlab,</i> Manolis Lourakis</li></ul>
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<div id=header><code>xoshiro</code> / <code>xoroshiro</code> generators and the PRNG shootout</div>
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<h1 class=first>Introduction</h1>
<p>This page describes some new pseudorandom number generators (PRNGs) we (David Blackman and I) have been working on recently, and
a shootout comparing them with other generators. Details about the generators can
be found in our <a
href="http://vigna.di.unimi.it/papers.php#BlVSLPNG">paper</a>. Information about my previous <code>xorshift</code>-based
generators can be found <a href="xorshift.php">here</a>, but they have been entirely superseded by the new ones, which
are faster <em>and</em> better. As part of our study, we developed a very strong <a href="hwd.php">test for Hamming-weight dependencies</a>
that gave a number of surprising results.
<h1>64-bit Generators</h1>
<P><a href="xoshiro256plusplus.c"><code>xoshiro256++</code></a>/<a href="xoshiro256starstar.c"><code>xoshiro256**</code></a>
(XOR/shift/rotate) are our <strong>all-purpose</strong>
generators (not <em>cryptographically secure</em> generators, though,
like all PRNGs in these pages). They have excellent (sub-ns) speed, a state
space (256 bits) that is large enough for any parallel application, and
they pass all tests we are aware of. See the <a href="http://vigna.di.unimi.it/papers.php#BlVSLPNG">paper</a>
for a discussion of their differences.
<p>If, however, one has to generate only 64-bit <strong>floating-point</strong> numbers
(by extracting the upper 53 bits) <a
href="xoshiro256plus.c"><code>xoshiro256+</code></a> is a slightly (&asymp;15%)
faster generator with analogous statistical properties. For general
usage, one has to consider that its lowest bits have low linear
complexity and will <a href="lowcomp.php">fail linearity tests</a>; however, low linear
complexity of the lowest bits can have hardly any impact in practice, and certainly has no
impact at all if you generate floating-point numbers using the upper bits (we computed a <a
href="http://vigna.di.unimi.it/papers.php#BlVSLPNG">precise
estimate</a> of the linear complexity of the lowest bits).
<p>If you are <strong>tight on space</strong>, <a
href="xoroshiro128plusplus.c"><code>xoroshiro128++</code></a>/<a
href="xoroshiro128starstar.c"><code>xoroshiro128**</code></a>
(XOR/rotate/shift/rotate) and <a
href="xoroshiro128plus.c"><code>xoroshiro128+</code></a> have the same
speed and use half of the space; the same comments apply. They are suitable only for
low-scale parallel applications; moreover, <code>xoroshiro128+</code>
exhibits a mild dependency in Hamming weights that generates a failure
after 5&thinsp;TB of output in <a href="hwd.php">our test</a>. We believe
this slight bias cannot affect any application.
<p>Finally, if for any reason (which reason?) you need <strong>more
state</strong>, we provide in the same
vein <a href="xoshiro512plusplus.c"><code>xoshiro512++</code></a> / <a href="xoshiro512starstar.c"><code>xoshiro512**</code></a> / <a href="xoshiro512plus.c"><code>xoshiro512+</code></a> and
<a href="xoroshiro1024plusplus.c"><code>xoroshiro1024++</code></a> / <a href="xoroshiro1024starstar.c"><code>xoroshiro1024**</code></a> / <a href="xoroshiro1024star.c"><code>xoroshiro1024*</code></a> (see the <a
href="http://vigna.di.unimi.it/papers.php#BlVSLPNG">paper</a>).
<p>All generators, being based on linear recurrences, provide <em>jump
functions</em> that make it possible to simulate any number of calls to
the next-state function in constant time, once a suitable <em>jump
polynomial</em> has been computed. We provide ready-made jump functions for
a number of calls equal to the square root of the period, to make it easy
generating non-overlapping sequences for parallel computations, and equal
to the cube of the fourth root of the period, to make it possible to
generate independent sequences on different parallel processors.
<p>We suggest to use <a href="splitmix64.c"><span
style="font-variant: small-caps">SplitMix64</span></a> to initialize
the state of our generators starting from a 64-bit seed, as <a href="https://dl.acm.org/citation.cfm?doid=1276927.1276928">research
has shown</a> that initialization must be performed with a generator
radically different in nature from the one initialized to avoid
correlation on similar seeds.
<h1>32-bit Generators</h1>
<P><a href="xoshiro128plusplus.c"><code>xoshiro128++</code></a>/<a href="xoshiro128starstar.c"><code>xoshiro128**</code></a> are our
<strong>32-bit</strong> all-purpose generators, whereas <a
href="xoshiro128plus.c"><code>xoshiro128+</code></a> is
for floating-point generation. They are the 32-bit counterpart of
<code>xoshiro256++</code>, <code>xoshiro256**</code> and <code>xoshiro256+</code>, so similar comments apply.
Their state is too small for
large-scale parallelism: their intended usage is inside embedded
hardware or GPUs. For an even smaller scale, you can use <a
href="xoroshiro64starstar.c"><code>xoroshiro64**</code></a> and <a
href="xoroshiro64star.c"><code>xoroshiro64*</code></a>. We not believe
at this point in time 32-bit generator with a larger state can be of
any use (but there are 32-bit <code>xoroshiro</code> generators of much larger size).
<p>All 32-bit generators pass all tests we are aware of, with the
exception of linearity tests (binary rank and linear complexity) for
<code>xoshiro128+</code> and <code>xoroshiro64*</code>: in this case,
due to the smaller number of output bits the low linear complexity of the
lowest bits is sufficient to trigger BigCrush tests when the output is bit-reversed. Analogously to
the 64-bit case, generating 32-bit floating-point number using the
upper bits will not use any of the bits with <a href="lowcomp.php">low linear complexity</a>.
<h1>16-bit Generators</h1>
<p>We do not suggest any particular 16-bit generator, but it is possible
to design relatively good ones using our techniques. For example,
Parallax has embedded in their <a href="https://www.parallax.com/propeller-2/">Propeller 2 microcontroller</a> multiple 16-bit
<code>xoroshiro32++</code> generators.
<h1>Congruential Generators</h1>
<p>In case you are interested in 64-bit PRNGs based on congruential arithmetic, I provide
three instances of a
<a href="https://groups.google.com/forum/#!searchin/sci.stat.math/Yet$20another$20rng%7Csort:date/sci.stat.math/p7aLW3TsJys/QGb1kti6kN0J">Marsaglia's Multiply-With-Carry generators</a>,
<a href="MWC128.c"><code>MWC128</code></a>, <a href="MWC192.c"><code>MWC192</code></a>, and <a href="MWC256.c"><code>MWC256</code></a>, for which I computed good constants. They are some
of the fastest generator available, but they need 128-bit operations.
<p>Stronger theoretical guarantees are provided by the
<a href="https://www.math.ias.edu/~goresky/pdf/p1-goresky.pdf">generalized multiply-with-carry generators defined by Goresky and Klapper</a>:
also in this case I provide two instances, <a href="GMWC128.c"><code>GMWC128</code></a> and <a href="GMWC256.c"><code>GMWC256</code></a>, for which I computed good constants.
This generators, however, are about twice slower than MWC generators.
<h1>JavaScript</h1>
<p><code>xorshift128+</code> is presently used in the JavaScript engines of
<a href="http://v8project.blogspot.com/2015/12/theres-mathrandom-and-then-theres.html">Chrome</a>,
<a href="https://nodejs.org/">Node.js</a>,
<a href="https://bugzilla.mozilla.org/show_bug.cgi?id=322529#c99">Firefox</a>,
<a href="https://bugs.webkit.org/show_bug.cgi?id=151641">Safari</a> and
<a href="https://github.com/Microsoft/ChakraCore/commit/dbda0182dc0a983dfb37d90c05000e79b6fc75b0">Microsoft Edge</a>.
<h1>Rust</h1>
<p>The <a href="https://docs.rs/rand/latest/rand/rngs/struct.SmallRng.html">SmallRng</a> from the <a href="https://docs.rs/rand/latest/rand/">rand</a>
crate is <a HREF="xoshiro256plusplus.c"><code>xoshiro256++</code></a> or <a HREF="xoshiro128plusplus.c"><code>xoshiro128++</code></a>, depending
on the platform.
<h1><code>java.util.random</code></h1>
<p>I worked with Guy Steele at the <a
href="https://docs.oracle.com/en/java/javase/17/docs/api/java.base/java/util/random/package-summary.html">new
family of PRNGs available in Java 17</a>. The family, called <a
href="http://vigna.di.unimi.it/papers.php#StVLXM">LXM</a>, uses <a
href="http://vigna.di.unimi.it/papers.php#StVCESGMCPNG">new, better
tables of multipliers for LCGs with power-of-two moduli</a>. Moreover,
<code>java.util.random</code> contains ready-to-use implementations of
<a HREF="xoroshiro128plusplus.c"><code>xoroshiro128++</code></a> and <a
HREF="xoshiro256plusplus.c"><code>xoshiro256++</code></a>.
<h1>.NET</h1>
<p>In version 6, Microsoft's .NET framework <a href="https://devblogs.microsoft.com/dotnet/performance-improvements-in-net-6/">has adopted</a>
<a HREF="xoshiro256starstar.c"><code>xoshiro256**</code></a> and <a
HREF="xoshiro128starstar.c"><code>xoshiro128**</code></a> as default PRNGs.
<h1>Erlang</h1>
<p>The parallel functional language <a href="https://www.erlang.org/">Erlang</a> implements <a href="https://www.erlang.org/doc/man/rand.html">several
variants of <code>xorshift</code>/<code>xoroshiro</code>-based generators</a> adapted in collaboration with Raimo Niskanen for Erlang's
58/59-bit arithmetic.
<h1>GNU FORTRAN</h1>
<p>GNU's <a href="https://gcc.gnu.org/fortran/">implementation of the FORTRAN language</a> <a href="https://gcc.gnu.org/onlinedocs/gfortran/RANDOM_005fNUMBER.html">uses</a>
<a HREF="xoshiro256starstar.c"><code>xoshiro256**</code></a> as default PRNG.
<h1>Julia</h1>
<p>The <a href="https://julialang.org/">Julia programming language</a> <a href="https://docs.julialang.org/en/v1/stdlib/Random/">uses</a>
<a HREF="xoshiro256plusplus.c"><code>xoshiro256++</code></a> as default PRNG.
<h1>Lua</h1>
<p>The scripting language <a href="http://www.lua.org/">Lua</a> <a href="https://www.lua.org/manual/5.4/manual.html#pdf-math.random">uses</a> <a HREF="xoshiro256starstar.c"><code>xoshiro256**</code></a> as default PRNG.
<h1>IoT</h1>
<p>The IoT operating systems <a href="https://os.mbed.com/">Mbed</a> and <a href="https://www.zephyrproject.org/">Zephyr</a> use
<a HREF="xoroshiro128plus.c"><code>xoroshiro128+</code></a> as default PRNG.
<h1><a name=shootout>&#xfeff;</a>A PRNG Shootout</h1>
<p>I provide here a shootout of a few recent 64-bit PRNGs that are quite widely used.
The purpose is that of providing a consistent, reproducible assessment of two properties of the generators: speed and quality.
The code used to perform the tests and all the output from statistical test suites is available for download.
<h2><a name=speed>&#xfeff;</a>Speed</h2>
<p>The speed reported in this page is the time required to emit 64
random bits, and the number of clock cycles required to generate a byte (thanks to the <a href="http://icl.utk.edu/papi/">PAPI</a> library). If a generator is 32-bit in nature, I glue two
consecutive outputs. Note that
I do not report results using GPUs or SSE instructions, with an exception for the very common SFMT: for that to be
meaningful, I should have implementations for all generators.
Otherwise, with suitable hardware support I could just use AES in
counter mode and get 64 secure bits in 0.56&thinsp;ns (or just use <a href="https://github.com/google/randen">Randen</a>). The tests were performed on a
12th Gen Intel&reg; Core&trade; i7-12700KF @3.60GHz using <code>gcc</code> 12.2.1.
<p>A few <i>caveats</i>:
<ul>
<li>There is some looping overhead, but subtracting it from the timings is not going to
be particularly meaningful due to instruction rescheduling, etc.
<li>Relative speed might be different on different CPUs and on different scenarios.
<li>I do not use <code>-march=native</code>, which can improve the timing of some generators
by vectorization or special instructions, because those improvements might not be possible
when the generator is embedded in user code.
<li>Code has been compiled using <code>gcc</code>'s <code>-fno-unroll-loops</code>
option. This options is essential to get a sensible result: without it, the compiler
may perform different loop unrolling depending on the generator. Previosuly I was using also
<code>-fno-move-loop-invariants</code>, which was essential in not giving generators using several
large constants an advantage by preventing the compiler from loading them into registers. However,
as of <code>gcc</code> 12.2.1 the compiler loads the constants into registers anyway, so the
option is no longer used. Timings
with <a href="http://clang.llvm.org/"><code>clang</code></a> at the time of this writing
are very close to those obtained with <code>gcc</code>.
If you find timings that are significantly better than those shown here on
comparable hardware, they are likely to be due to compiler artifacts (e.g., vectorization).
<li>Timings are taken running a generator for billions of times in a loop; but this is not the way you use generators. Register
allocation might be very different when the generator is embedded in an application, leading to constants being reloaded
or part of the state space being written to main memory at each iteration. These costs do not appear in the benchmarks below.
</ul>
<p>To ease replicability, I distribute a <a href="harness.c"><em>harness</em></a> performing the measurement. You just
have to define a <a href="xoroshiro128plus-speed.c"><code>next()</code></a> function and include the harness. But the only realistic
suggestion is to try different generators in your application and see what happens.
<h2><a name=quality>&#xfeff;</a>Quality</h2>
<p>This is probably the more <a
href="http://dilbert.com/strips/comic/2001-10-25/">elusive</a> property
of a PRNG. Here quality is measured using the powerful
BigCrush suite of tests. BigCrush is part of <a
href="http://simul.iro.umontreal.ca/testu01/tu01.html">TestU01</a>,
a monumental framework for testing PRNGs developed by Pierre L'Ecuyer
and Richard Simard (&ldquo;TestU01: A C library for empirical testing
of random number generators&rdquo;, <i>ACM Trans. Math. Softw.</i>
33(4), Article 22, 2007).
<p>I run BigCrush starting from 100 equispaced points of the state space
of the generator and collect <em>failures</em>&mdash;tests in which the
<i>p</i>-value statistics is outside the interval [0.001..0.999]. A failure
is <em>systematic</em> if it happens at all points.
<p>Note that TestU01 is a 32-bit test suite. Thus, two 32-bit integer values
are passed to the test suite for each generated 64-bit value. Floating point numbers
are generated instead by dividing the unsigned output of the generator by 2<sup>64</sup>.
Since this implies a bias towards the high bits (which is anyway a known characteristic
of TestU01), I run the test suite also on the <em>reverse</em>
generator. More detail about the whole process can be found in this <a
href="http://vigna.di.unimi.it/papers.php#VigEEMXGS">paper</a>.
<p>Beside BigCrush, I analyzed generators using a test for <a href="hwd.php">Hamming-weight dependencies</a>
described in our <a href="http://vigna.di.unimi.it/papers.php#BlVNTHWD">paper</a>. As I already remarked, our only
generator failing the test (but only after 5&thinsp;TB of output) is <code>xoroshiro128+</code>.
<p>I report the period of each generator and its footprint in bits: a generator gives &ldquo;bang-for-the-buck&rdquo;
if the base-2 logarithm of the period is close to the footprint. Note
that the footprint has been always padded to a multiple of 64, and it can
be significantly larger than expected because of padding and
cyclic access indices.
<div style="align: center"><table id='prng' style='margin: 2em 0' class='tablesorter'>
<thead><tr>
<th>PRNG
<th>Footprint (bits)
<th class="{ sorter: 'metadata' }">Period
<th><a href="http://simul.iro.umontreal.ca/testu01/tu01.html">BigCrush</a> Systematic Failures
<th><a href="http://prng.di.unimi.it/hwd.php">HWD failure</a>
<th>ns/64 bits
<th>cycles/B
<tbody>
<tr><td><a href="xoroshiro128plus.c"><code>xoroshiro128+</code></a><td align=right>128 <td align=right class='{sortValue: 128}'>2<sup>128</sup>&nbsp;&minus;&nbsp;1<td align=right>&mdash;<td align=right>5&thinsp;TB<td align=right>0.80<td align=right>0.36
<tr><td><a href="xoroshiro128plusplus.c"><code>xoroshiro128++</code></a><td align=right>128 <td align=right class='{sortValue: 128}'>2<sup>128</sup>&nbsp;&minus;&nbsp;1<td align=right>&mdash;<td align=right>&mdash;<td align=right>0.90<td align=right>0.40
<tr><td><a href="xoroshiro128starstar.c"><code>xoroshiro128**</code></a><td align=right>128 <td align=right class='{sortValue: 128}'>2<sup>128</sup>&nbsp;&minus;&nbsp;1<td align=right>&mdash;<td align=right>&mdash;<td align=right>0.78<td align=right>0.36
<tr><td><a href="xoshiro256plus.c"><code>xoshiro256+</code></a><td align=right>256 <td align=right class='{sortValue: 256}'>2<sup>256</sup>&nbsp;&minus;&nbsp;1<td align=right>&mdash;<td align=right>&mdash;<td align=right>0.61<td align=right>0.27
<tr><td><a href="xoshiro256plusplus.c"><code>xoshiro256++</code></a><td align=right>256 <td align=right class='{sortValue: 256}'>2<sup>256</sup>&nbsp;&minus;&nbsp;1<td align=right>&mdash;<td align=right>&mdash;<td align=right>0.75<td align=right>0.34
<tr><td><a href="xoshiro256starstar.c"><code>xoshiro256**</code></a><td align=right>256 <td align=right class='{sortValue: 256}'>2<sup>256</sup>&nbsp;&minus;&nbsp;1<td align=right>&mdash;<td align=right>&mdash;<td align=right>0.75<td align=right>0.34
<tr><td><a href="xoshiro512plus.c"><code>xoshiro512+</code></a><td align=right>512<td align=right class='{sortValue: 512}'>2<sup>512</sup>&nbsp;&minus;&nbsp;1<td align=right>&mdash;<td align=right>&mdash;<td align=right>0.68<td align=right>0.30
<tr><td><a href="xoshiro512plusplus.c"><code>xoshiro512++</code></a><td align=right>512<td align=right class='{sortValue: 512}'>2<sup>512</sup>&nbsp;&minus;&nbsp;1<td align=right>&mdash;<td align=right>&mdash;<td align=right>0.79<td align=right>0.36
<tr><td><a href="xoshiro512starstar.c"><code>xoshiro512**</code></a><td align=right>512<td align=right class='{sortValue: 512}'>2<sup>512</sup>&nbsp;&minus;&nbsp;1<td align=right>&mdash;<td align=right>&mdash;<td align=right>0.81<td align=right>0.37
<tr><td><a href="xoroshiro1024star.c"><code>xoroshiro1024*</code></a><td align=right>1068<td align=right class='{sortValue: 1024}'>2<sup>1024</sup>&nbsp;&minus;&nbsp;1<td align=right>&mdash;<td align=right>&mdash;<td align=right>0.82<td align=right>0.37
<tr><td><a href="xoroshiro1024plusplus.c"><code>xoroshiro1024++</code></a><td align=right>1068<td align=right class='{sortValue: 1024}'>2<sup>1024</sup>&nbsp;&minus;&nbsp;1<td align=right>&mdash;<td align=right>&mdash;<td align=right>1.01<td align=right>0.46
<tr><td><a href="xoroshiro1024starstar.c"><code>xoroshiro1024**</code></a><td align=right>1068<td align=right class='{sortValue: 1024}'>2<sup>1024</sup>&nbsp;&minus;&nbsp;1<td align=right>&mdash;<td align=right>&mdash;<td align=right>0.98<td align=right>0.44
<tr><td><a href="MWC128.c"><span style="font-variant: small-caps">MWC128</span></a><td align=right>128 <td align=right class='{sortValue: 127}'>&asymp;2<sup>127</sup><td align=right>&mdash;<td align=right>&mdash;<td align=right>0.83<td align=right>0.37
<tr><td><a href="MWC192.c"><span style="font-variant: small-caps">MWC192</span></a><td align=right>192 <td align=right class='{sortValue: 127}'>&asymp;2<sup>191</sup><td align=right>&mdash;<td align=right>&mdash;<td align=right>1.42<td align=right>0.19
<tr><td><a href="MWC256.c"><span style="font-variant: small-caps">MWC256</span></a><td align=right>256 <td align=right class='{sortValue: 255}'>&asymp;2<sup>255</sup><td align=right>&mdash;<td align=right>&mdash;<td align=right>0.45<td align=right>0.20
<tr><td><a href="GMWC128.c"><span style="font-variant: small-caps">GMWC128</span></a><td align=right>128 <td align=right class='{sortValue: 127}'>&asymp;2<sup>127</sup><td align=right>&mdash;<td align=right>&mdash;<td align=right>1.84<td align=right>0.83
<tr><td><a href="GMWC256.c"><span style="font-variant: small-caps">GMWC256</span></a><td align=right>256 <td align=right class='{sortValue: 255}'>&asymp;2<sup>255</sup><td align=right>&mdash;<td align=right>&mdash;<td align=right>1.85<td align=right>0.83
<tr><td><a href="http://pracrand.sourceforge.net/"><span style="font-variant: small-caps">SFC64</span></a><td align=right>256 <td align=right class='{sortValue: 64}'>&ge;2<sup>64</sup><td align=right>&mdash;<td align=right>&mdash;<td align=right>0.66<td align=right>0.30
<tr><td><a href="splitmix64.c"><span style="font-variant: small-caps">SplitMix64</span></a><td align=right>64 <td align=right class='{sortValue: 64}'>2<sup>64</sup><td align=right>&mdash;<td align=right>&mdash;<td align=right>0.63<td align=right>0.29
<tr><td><a href="http://pcg-random.org/">PCG 128 XSH RS 64 (LCG)</a> <td align=right>128 <td align=right class='{sortValue: 128}'>2<sup>128</sup><td align=right>&mdash;<td align=right>&mdash;<td align=right>1.70<td align=right>0.77
<tr><td><a href="https://github.com/numpy/numpy">PCG64-DXSM (NumPy)</a> <td align=right>128 <td align=right class='{sortValue: 128}'>2<sup>128</sup><td align=right>&mdash;<td align=right>&mdash;<td align=right>1.11<td align=right>0.50
<tr><td><a href="http://numerical.recipes/"><code>Ran</code></a><td align=right>192 <td align=right class='{sortValue: 191}'>&#8776;2<sup>191</sup><td align=right>&mdash;<td align=right>&mdash;<td align=right>1.37<td align=right>0.62
<tr><td><a href="http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html"><code>MT19937-64</code> (Mersenne Twister)</a><td align=right>20032 <td align=right class='{sortValue: 19937}'>2<sup>19937</sup>&nbsp;&minus;&nbsp;1<td align=right>LinearComp<td align=right>&mdash;<td align=right>1.36<td align=right>0.62
<tr><td><a href="http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/"><code>SFMT19937 (uses SSE2 instructions)</code></a><td align=right>20032 <td align=right class='{sortValue: 19937}'>2<sup>19937</sup>&nbsp;&minus;&nbsp;1<td align=right>LinearComp<td align=right>&mdash;<td align=right>0.93<td align=right>0.42
<tr><td><a href="http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/"><code>SFMT607 (uses SSE2 instructions)</code></a><td align=right>672 <td align=right class='{sortValue: 607}'>2<sup>607</sup>&nbsp;&minus;&nbsp;1<td align=right>MatrixRank, LinearComp<td align=right>400 MB<td align=right>0.78<td align=right>0.34
<tr><td><a href="http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/TINYMT/index.html">Tiny Mersenne Twister</a> (64 bits)<td align=right>256<td align=right class='{sortValue: 127}'>2<sup>127</sup>&nbsp;&minus;&nbsp;1<td align=right>&mdash;<td align=right>90&thinsp;TB→<td align=right>2.76<td align=right>1.25
<tr><td><a href="http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/TINYMT/index.html">Tiny Mersenne Twister</a> (32 bits)<td align=right>224<td align=right class='{sortValue: 127}'>2<sup>127</sup>&nbsp;&minus;&nbsp;1<td align=right>CollisionOver, Run, SimPoker, AppearanceSpacings, MatrixRank, LinearComp, LongestHeadRun, Run of Bits (reversed)<td align=right>40&thinsp;TB→<td align=right>4.27<td align=right>1.92
<tr><td><a href="http://www.iro.umontreal.ca/~panneton/WELLRNG.html"><code>WELL512a</code></a><td align=right>544 <td align=right class='{sortValue: 512}'>2<sup>512</sup>&nbsp;&minus;&nbsp;1 <td align=right>MatrixRank, LinearComp<td align=right>3.5 PB<td align=right>5.42<td align=right>2.44
<tr><td><a href="http://www.iro.umontreal.ca/~panneton/WELLRNG.html"><code>WELL1024a</code></a><td align=right>1056 <td align=right class='{sortValue: 1024}'>2<sup>1024</sup>&nbsp;&minus;&nbsp;1 <td align=right>MatrixRank, LinearComp<td align=right>&mdash;<td align=right>5.30<td align=right>2.38
</table></div>
<p>The following table compares instead two ways of generating floating-point numbers, namely the 521-bit <a href="http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/">dSFMT</a>, which
generates directly floating-point numbers with 52 significant bits, and
<a href="xoshiro256plus.c"><code>xoshiro256+</code></a> followed by a standard conversion of its upper bits to a floating-point number with 53 significant bits (see below).
<div style="align: center"><table id='prngf' style='margin: 2em 0' class='tablesorter'>
<thead><tr>
<th>PRNG
<th>Footprint (bits)
<th class="{ sorter: 'metadata' }">Period
<th> <a href="http://simul.iro.umontreal.ca/testu01/tu01.html">BigCrush</a> Systematic Failures
<th><a href="http://prng.di.unimi.it/hwd.php">HWD failure</a>
<th>ns/double
<th>cycles/B
<tbody>
<tr><td><a href="xoshiro256plus.c"><code>xoshiro256+</code></a> (returns 53 significant bits) <td align=right>256<td align=right class='{sortValue: 256}'>2<sup>256</sup>&nbsp;&minus;&nbsp;1<td align=right>&mdash;<td align=right>&mdash;<td align=right>0.92<td align=right>3.40
<tr><td><a href="http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/"><code>dSFMT</code></a> (uses SSE2 instructions, returns only 52 significant bits)<td align=right>704<td align=right class='{sortValue: 521}'>2<sup>521</sup>&nbsp;&minus;&nbsp;1<td align=right>MatrixRank, LinearComp<td align=right>6&thinsp;TB<td align=right>0.85<td align=right>3.07
</table></div>
<p><code>xoshiro256+</code> is &asymp;8% slower than the dSFMT, but it has a doubled range of output values, does not need any extra SSE instruction (can be programmed in Java, etc.),
has a much smaller footprint, and its upper bits do not fail any test.
<h1><a name=remarks>&#xfeff;</a>Remarks</h1>
<h2>Vectorization</h2>
<p>Some of the generators can be very easily vectorized, so that multiple instances can be run in parallel to provide
fast bulk generation. Thanks to an interesting <a href="https://github.com/JuliaLang/julia/issues/27614">discussion with the Julia developers</a>,
I've become aware that AVX2 vectorizations of multiple instances of generators using the <code>+</code>/<code>++</code> scrambler are impressively fast (links
below point at a speed test to be used with the <a href="harness.c">harness</a>, and the result will be multiplied by 1000):
<div style="align: center"><table id='vec' style='margin: 2em 0' class='tablesorter'>
<thead><tr>
<th>PRNG
<th>ns/64 bits
<th>cycles/B
<tbody>
<tr><td><a href="xoroshiro128+-vect-speed.c"><code>xoroshiro128+</code></a> (4 parallel instances)<td align=right>0.36<td align=right>0.14
<tr><td><a href="xoroshiro128++-vect-speed.c"><code>xoroshiro128++</code></a> (4 parallel instances)<td align=right>0.45<td align=right>0.18
<tr><td><a href="xoshiro256+-vect-speed.c"><code>xoshiro256+</code></a> (8 parallel instances)<td align=right>0.19<td align=right>0.08
<tr><td><a href="xoshiro256++-vect-speed.c"><code>xoshiro256++</code></a> (8 parallel instances)<td align=right>0.26<td align=right>0.09
</table></div>
<p>Note that sometimes convincing the compiler to vectorize is a
slightly quirky process: for example, on <code>gcc</code> 12.2.1 I have to use <code>-O3 -fdisable-tree-cunrolli -march=native</code>
to vectorize <code>xoshiro256</code>-based generators
(<code>-O3</code> alone will not vectorize; thanks to to Chris Elrod for pointing me at <code>-fdisable-tree-cunrolli</code>).
<h2>A long period does not imply high quality</h2>
<p>This is a common misconception. The generator <code>x++</code> has
period \(2^k\), for any \(k\geq0\), provided that <code>x</code> is
represented using \(k\) bits: nonetheless, it is a horrible generator.
The generator returning \(k-1\) zeroes followed by a one has period
\(k\).
<p>It is however important that the period is long enough. A first heuristic rule of thumb
is that if you need to use \(t\) values, you need a generator with period at least \(t^2\).
Moreover, if you run \(n\) independent computations starting at random seeds,
the sequences used by each computation should not overlap.
<p>Now, given a generator with period \(P\), the probability that \(n\) subsequences of length \(L\) starting at random points in the state space
overlap <a href="http://vigna.di.unimi.it/papers.php#VigPORSPNG">is bounded by \(n^2L/P\)</a>. If your generator has period \(2^{256}\) and you run
on \(2^{64}\) cores (you will never have them) a computation using \(2^{64}\) pseudorandom numbers (you will never have the time)
the probability of overlap would be less than \(2^{-64}\).
<p>In other words: any generator with a period beyond
\(2^{256}\) has a period that is
sufficient for every imaginable application. Unless there are other motivations (e.g., provably
increased quality), a generator with a larger period is only a waste of
memory (as it needs a larger state), of cache lines, and of
precious high-entropy random bits for seeding (unless you're using
small seeds, but then it's not clear why you would want a very long
period in the first place&mdash;the computation above is valid only if you seed all bits of the state
with independent, uniformly distributed random bits).
<p>In case the generator provides a <em>jump function</em> that lets you skip through chunks of the output in constant
time, even a period of \(2^{128}\) can be sufficient, as it provides \(2^{64}\) non-overlapping sequences of length \(2^{64}\).
<h2>Equidistribution</h2>
<p>Every 64-bit generator of ours with <var>n</var> bits of state scrambled
with <code>*</code> or <code>**</code> is <var>n</var>/64-dimensionally
equidistributed: every <var>n</var>/64-tuple of consecutive 64-bit
values appears exactly once in the output, except for the zero tuple
(and this is the largest possible dimension). Generators based on the
<code>+</code> or <code>++</code> scramblers are however only (<var>n</var>/64 &minus;
1)-dimensionally equidistributed: every (<var>n</var>/64 &minus;
1)-tuple of consecutive 64-bit values appears exactly 2<sup>64</sup>
times in the output, except for a missing zero tuple. The same considerations
apply to 32-bit generators.
<h2>Generating uniform doubles in the unit interval</h2>
<p>A standard double (64-bit) floating-point number in
<a href="https://en.wikipedia.org/wiki/IEEE_floating_point">IEEE floating point format</a> has 52 bits of
significand, plus an implicit bit at the left of the significand. Thus,
the representation can actually store numbers with <em>53</em> significant binary digits.
<p>Because of this fact, in C99 a 64-bit unsigned integer <code>x</code> should be converted to a 64-bit double
using the expression
<pre>
#include &lt;stdint.h>
(x >> 11) * 0x1.0p-53
</pre>
<p>In Java you can use almost the same expression for a (signed) 64-bit integer:
<pre>
(x >>> 11) * 0x1.0p-53
</pre>
<p>This conversion guarantees that all dyadic rationals of the form <var>k</var> / 2<sup>&minus;53</sup>
will be equally likely. Note that this conversion prefers the high bits of <code>x</code> (usually, a good idea), but you can alternatively
use the lowest bits.
<p>An alternative, multiplication-free conversion is
<pre>
#include &lt;stdint.h>
static inline double to_double(uint64_t x) {
const union { uint64_t i; double d; } u = { .i = UINT64_C(0x3FF) &lt;&lt; 52 | x >> 12 };
return u.d - 1.0;
}
</pre>
<p>The code above cooks up by bit manipulation
a real number in the interval [1..2), and then subtracts
one to obtain a real number in the interval [0..1). If <code>x</code> is chosen uniformly among 64-bit integers,
<code>d</code> is chosen uniformly among dyadic rationals of the form <var>k</var> / 2<sup>&minus;52</sup>. This
is the same technique used by generators providing directly doubles, such as the
<a href="http://dx.doi.org/10.1007/978-3-540-85912-3_26">dSFMT</a>.
<p>This technique is supposed to be fast, but on recent hardare it does not seem to give a significant advantage.
More importantly, <em>you will be generating half the values you could actually generate</em>.
The same problem plagues the dSFMT. All doubles generated will have the lowest significand bit set to zero (I must
thank Raimo Niskanen from the Erlang team for making me notice this&mdash;a previous version of this site
did not mention this issue).
<p>In Java you can obtain an analogous result using suitable static methods:
<pre>
Double.longBitsToDouble(0x3FFL &lt;&lt; 52 | x >>> 12) - 1.0
</pre>
<p>To adhere to the principle of least surprise, my implementations now use the multiplicative version, everywhere.
<p>Interestingly, these are not the only notions of &ldquo;uniformity&rdquo; you can come up with. Another possibility
is that of generating 1074-bit integers, normalize and return the nearest value representable as a
64-bit double (this is the theory&mdash;in practice, you will almost never
use more than two integers per double as the remaining bits would not be representable). This approach guarantees that all
representable doubles could be in principle generated, albeit not every
returned double will appear with the same probability. A reference
implementation can be found <a href="random_real.c">here</a>. Note that unless your generator has
at least 1074 bits of state and suitable equidistribution properties, the code above will not do what you expect
(e.g., it might <em>never</em> return zero).
</div>
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<h1>C code (64 bits)</h1>
<p><ul>
<li><a HREF="xoshiro256plusplus.c"><code>xoshiro256++</code></a>
<li><a HREF="xoshiro256starstar.c"><code>xoshiro256**</code></a>
<li><a HREF="xoshiro256plus.c"><code>xoshiro256+</code></a>
<li><a HREF="xoroshiro128plusplus.c"><code>xoroshiro128++</code></a>
<li><a HREF="xoroshiro128starstar.c"><code>xoroshiro128**</code></a>
<li><a HREF="xoroshiro128plus.c"><code>xoroshiro128+</code></a>
<li><a HREF="https://github.com/vigna/MRG32k3a">Testless <code>MRG32k3a</code></a>
<li><a HREF="MWC128.c"><code>MWC128</code></a> + <a HREF="mp.c"><code>mp.c</code></a>
<li><a HREF="MWC192.c"><code>MWC192</code></a> + <a HREF="mp.c"><code>mp.c</code></a>
<li><a HREF="MWC256.c"><code>MWC256</code></a> + <a HREF="mp.c"><code>mp.c</code></a>
<li><a HREF="GMWC128.c"><code>GMWC128</code></a> + <a HREF="mp.c"><code>mp.c</code></a>
<li><a HREF="GMWC256.c"><code>GMWC256</code></a> + <a HREF="mp.c"><code>mp.c</code></a>
<!-- <li><a HREF="xoshiro512starstar.c"><code>xoshiro512**</code></a>
<li><a HREF="xoshiro512plus.c"><code>xoshiro512+</code></a>
<li><a HREF="xoroshiro1024starstar.c"><code>xoroshiro1024**</code></a>
<li><a HREF="xoroshiro1024plus.c"><code>xoroshiro1024*</code></a>-->
</ul>
<h1>C code (32 bits)</h1>
<p><ul>
<li><a HREF="xoshiro128plusplus.c"><code>xoshiro128++</code></a>
<li><a HREF="xoshiro128starstar.c"><code>xoshiro128**</code></a>
<li><a HREF="xoshiro128plus.c"><code>xoshiro128+</code></a>
<li><a HREF="xoroshiro64starstar.c"><code>xoroshiro64**</code></a>
<li><a HREF="xoroshiro64star.c"><code>xoroshiro64*</code></a>
</ul>
<h1>Java code (<a HREF="https://github.com/openjdk/jdk17/tree/master/src/jdk.random/share/classes/jdk/random"><code>java.util.random</code></a>)</h1>
<h1>Java code (<a href="http://dsiutils.di.unimi.it">DSI utilities</a>)</h1>
<p><ul>
<li><a HREF="http://dsiutils.di.unimi.it/docs/it/unimi/dsi/util/package-summary.html">Overview</a>
<li><a HREF="http://dsiutils.di.unimi.it/docs/it/unimi/dsi/util/XoShiRo256PlusPlusRandom.html"><code>xoshiro256++</code></a>
<li><a HREF="http://dsiutils.di.unimi.it/docs/it/unimi/dsi/util/XoShiRo256StarStarRandom.html"><code>xoshiro256**</code></a>
<li><a HREF="http://dsiutils.di.unimi.it/docs/it/unimi/dsi/util/XoShiRo256PlusRandom.html"><code>xoshiro256+</code></a>
<li><a HREF="http://dsiutils.di.unimi.it/docs/it/unimi/dsi/util/XoRoShiRo128PlusPlusRandom.html"><code>xoroshiro128++</code></a>
<li><a HREF="http://dsiutils.di.unimi.it/docs/it/unimi/dsi/util/XoRoShiRo128StarStarRandom.html"><code>xoroshiro128**</code></a>
<li><a HREF="http://dsiutils.di.unimi.it/docs/it/unimi/dsi/util/XoRoShiRo128PlusRandom.html"><code>xoroshiro128+</code></a>
<li><a HREF="https://github.com/vigna/MRG32k3a">Testless <code>MRG32k3a</code></a>
</ul>
<h1>Java code (<a HREF="https://gitbox.apache.org/repos/asf?p=commons-rng.git">Apache Commons RNG implementations</a>)</h1>
<h1>Documentation</h1>
<p><ul>
<li>The <a href="http://vigna.di.unimi.it/papers.php#BlVSLPNG">paper</a> introducing <code>xoshiro</code>/<code>xoroshiro</code>.
<li>The <a href="http://vigna.di.unimi.it/papers.php#BlVNTHWD">paper</a> describing our <a href="hwd.php">test for Hamming-weight dependencies</a>.
<li>A <a href="http://vigna.di.unimi.it/papers.php#VigHTLGMT">paper</a> discussing the defects of the Mersenne Twister family of PRNGs.
<li>A <a href="http://vigna.di.unimi.it/papers.php#VigPORSPNG">paper</a> discussing the probability of overlap of random subsequences.
<li>A <a href="http://vigna.di.unimi.it/papers.php#StVCESGMCPNG">paper</a> with new tables of multipliers for LCGs with power-of-two moduli.
<li>A <a href="http://vigna.di.unimi.it/papers.php#StVLXM">paper</a> presenting the family LXM of PRNGs.
</ul>
<h1>Discussion</h1>
<p>There is a <a href="http://groups.google.com/group/prng">discussion group</a>
about this page. You can join or <a href="mailto:prng@googlegroups.com">send a message</a>.
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<article class="post-text h-entry hentry postpage col-md-8" itemscope="itemscope" itemtype="http://schema.org/Article"><header><h1 class="p-name entry-title" itemprop="headline name"><a href="#" class="u-url">On Vigna's PCG Critique</a></h1>
<div class="metadata">
<p class="byline author vcard"><span class="byline-name fn" itemprop="author">
M.E. O'Neill
</span></p>
<p class="dateline"><a href="#" rel="bookmark"><time class="published dt-published" datetime="2018-05-25T16:49:25-07:00" itemprop="datePublished" title="2018-05-25 16:49">2018-05-25 16:49</time></a></p>
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<p>On 14 May 2018, Sebastiano Vigna added <a href="http://pcg.di.unimi.it/pcg.php">a page to his website</a> (archived <a href="http://archive.is/VE0sX">here</a>) entitled “<em>The wrap-up on PCG generators</em>” that attempts to persuade readers to avoid various PCG generators.</p>
<p>That day, he also submitted a link to his critique to <a href="https://www.reddit.com/r/programming/comments/8jbkgy/the_wrapup_on_pcg_generators/">Reddit</a> (archived <a href="http://archive.is/b76dd">here</a>). I think it is fair to say that his remarks did not get quite the reception he might have hoped for. Readers mostly seemed to infer a certain animosity in his tone and his criticisms gained little traction with that audience.</p>
<p>Although I'm pleased to see readers of Reddit thinking critically about these things, it is worth taking the time to dive in and see what what lessons we can learn from all of this.</p>
<!-- TEASER_END -->
<h3 id="background">Background</h3>
<p>We have to feel a little sympathy for Vigna. On May 4, he updated <a href="http://xoshiro.di.unimi.it/">his website</a> to announce a new generation scheme, <em>Xoshiro</em> and accompanying <a href="http://vigna.di.unimi.it/ftp/papers/ScrambledLinear.pdf">paper</a>, the product of two years of work. He posted a link to his work <a href="https://www.reddit.com/r/programming/comments/8gx2d3/new_line_of_fast_prngs_released_by_the_author_of/">on Reddit</a> (archived <a href="http://archive.is/lv3js">here</a> and <a href="http://archive.is/1UULl">here</a>), and although he got some praise and thanks for his work, he ended up spending quite a lot of time talking not about his new work, but about flaws in his old work and about my work.</p>
<p>Here is an example of the kind of remarks he had to contend with; Reddit user “TomatoCo” wrote:</p>
<blockquote>
<p>I liked xoroshiro a lot until I read all of the dire condemnations of it, so I switched to PCG. I'm not a mathematician, I can't understand your papers and PCG's write ups are a lot easier to understand. I'm sure that you've analyzed the shit out of your previous generator and I can see on your site you've come up with new techniques to measure if xoshiro suffers the same flaws. But once bitten, twice shy. Xoroshiro was defended as great with the sole exception of the lowest bit. But then it was "the lowest bit is just a LSFR, so don't use that. Well, actually, the other low bits are also just really long period LSFRs, well, actually," and new flaws were constantly appearing.
Respectfully, I think you need to explain more and in simpler terms to earn everyone's trust back.</p>
<p>The reason I picked PCG was because its author could, in plain language, describe its behavior and why some authors witnessed patterns in your RNG.</p>
</blockquote>
<p>I think it's quite understandable that Vigna would want to look for ways to take PCG (and me) down a peg or two, and in various comment replies he endeavored to express things he didn't like about PCG (and the PCG website).</p>
<p>Most of the issues he raised were, I thought, adequately addressed and refuted in the Reddit discussion, but having gone to the effort already to try to articulate the things he did not like, even writing code to do so, it makes sense that he would want circulate these thoughts more broadly.</p>
<h3 id="reddit-reaction-2">Reddit Reaction #2</h3>
<p>Reddit's reaction to Vigna's new PCG-critique page was perhaps not what he hoped for. From what I can tell, pretty much none of the commenters were persuaded by his claims, and much was made of his tone.</p>
<p>Regarding tone, user “notfancy” said:</p>
<blockquote>
<p>Take your feud somewhere else. […] theory and practice definitely belong here. The petty squabbling and the name calling definitely don't. Seeing that Vigna himself is posting links to his own site, this is to me self-promoting spam.</p>
</blockquote>
<p>and user “foofel” added:</p>
<blockquote>
<p>the style in which he presents his stuff is always full of hate and despise, that's not a good way to represent it and probably why people are fed up.</p>
</blockquote>
<p>and user “evand” added:</p>
<blockquote>
<p>I would describe a lot of it as written very... condescendingly. There's also a lot that is written to attack her and not PCG</p>
</blockquote>
<p>and user “AntiauthoritarianNow” chimed in, saying;</p>
<blockquote>
<p>Yeah, it's one thing to tease other researchers a little bit, but this guy has a real problem sticking to arguments on the merits rather than derailing into reddit-esque ad-hom.</p>
</blockquote>
<p>But the thread also had plenty of rebuttals. For just about every claim Vigna had made in his critique, there was a comment explaining why the claim was flawed.</p>
<h3 id="my-reaction">My Reaction</h3>
<p>I could settle back into my chair here, and say, “Thank you, Reddit, for keeping your wits about you!”, but since (at the time of writing) Vigna's page remains live with the same claims, it seems sensible for me to create my own writeup (this one) to address his claims directly.</p>
<p>Moreover, I believe firmly that although it's never much fun to be on the receiving end for invective or personal attacks, in academia peer critique makes everything stronger. While much of what Vigna says about PCG doesn't hold up to closer scrutiny, it is worth trying to find value of some kind in every criticism. I believe in the approach taken in the world of improvisational comedy, known as <a href="https://en.wikipedia.org/wiki/Yes,_and...">“Yes, and…”</a>, which suggests that a participant should accept what another participant has stated (“yes”) and then expand on that line of thinking (“and”).</p>
<p>Thus, in the subsequent sections, I'll look at each of Vigna's critiques, first give a defensive response, and then endeavor to find a way to say “Yes, and…” to each one.</p>
<h3 id="correlations-due-to-contrived-seeding">Correlations Due to Contrived Seeding</h3>
<p>Vigna's first two claims relate to creating two PCG generators whose outputs are correlated because he has specifically set them up to have internal states that would cause them to be correlated.</p>
<h4 id="pcg-ext-variants-single-bit-change-to-the-extension-array">PCG <code>ext</code> Variants: Single Bit Change to the Extension Array</h4>
<p>In the first claim, he modifies the code for the PCG of the extended generation scheme to so that he can flip a single bit in the extension array that adds <em>k</em>-dimensional equidistribution to a base generator.</p>
<p>Vigna creates two <code>pcg64_k32</code> generators that are the same in all respects except for a single bit difference in one element of the 32-element extension array, and then observes that 31 of every 32 outputs will remain identical between the generators for some considerable time. Vigna clearly considers this behavior to be problematic and notes multiple LFSR-based PRNGs where such behavior would not occur.</p>
<p>Vigna states</p>
<blockquote>
<p>Said otherwise, the whole sequence of the generator is made by an enormous number of strongly correlated, very short sequences. And this makes the correlation tests fail.</p>
</blockquote>
<p>Vigna concludes that no one should use generators like <code>pcg64_k32</code> as a result.</p>
<h5 id="defensive-response">Defensive Response</h5>
<p>Vigna actually created a <em>custom version</em> of the PCG code to effect his single bit change. The <code>pcg64_k32</code> generator has 2303 bits of state, 127 bits of LCG increment (which stays constant), 128 bits of LCG current state, and 32 64-bit words in the extension array. The odds of seeding two <code>pcg64_k32</code> generators each with 2303 bits of seed entropy and finding that they only differ by a single bit in the extension array is 1 in 2<sup>2292</sup>, an order of magnitude so vast that it cannot be represented as a floating point double.</p>
<p>If the PRNG were properly initialized (e.g., using <code>std::seed_seq</code> or <code>pcg_extras::sed_seq_from&lt;std::random_device&gt;</code>), Vigna's observed correlation would not have occurred. Likewise, had the single bit change been in the LCG side of the PRNG, it would also not have occurred.</p>
<p>But what of Vigna's other claim, that PRNGs that are slow to diffuse single-bit changes to their internal state are necessarily bad? Vigna is right that for LFSR-based designs, the rate of bit diffusion (a.k.a. “<em>avalanche</em>”) matters a lot.</p>
<p>However, numerous perfectly good designs for PRNGs would fail Vigna's criteria. All counter-based designs (e.g., SplitMix, Random123, Chacha) will preserve the single bit difference indefinitely if we examine their internal state. In fact, Vigna's collaborator, David Blackman, is author of <code>gjrand</code>, which also includes a counter whose internal state won't diverge significantly over time. But of these designs, only SplitMix would fail a test that looks for output correlations rather than similar internal states.</p>
<p>The closest design to PCG's extension array is found in George Marsaglia's venerable <a href="https://en.wikipedia.org/wiki/Xorshift#xorwow">XorWow PRNG</a>, shown below (code taken from the Wikipedia page):</p>
<pre><code>/* The state array must be initialized to not be all zero in the first four
words */
uint32_t xorwow(uint32_t state[static 5])
{
/* Algorithm "xorwow" from p. 5 of Marsaglia, "Xorshift RNGs" */
uint32_t s, t = state[3];
t ^= t &gt;&gt; 2;
t ^= t &lt;&lt; 1;
state[3] = state[2]; state[2] = state[1]; state[1] = s = state[0];
t ^= s;
t ^= s &lt;&lt; 4;
state[0] = t;
return t + (state[4] += 362437);
}
</code></pre>
<p>In Marsaglia's design, <code>state[4]</code> is a counter in much the same way that PCG's extension array is a “funky counter”. Marsaglia calls this counter a <em>Weyl sequence</em> after Hermann Weyl, who proved the equidistribution theorem in 1916.</p>
<p>We can exactly reproduce Vigna's claim's about <code>pcg64_k32</code> producing similar output with XorWow. The program <a href="../downloads/snippets/uncxorwow.c"><code>uncxorwow.c</code></a> is a port of his demonstration program to XorWow. It fails if tested with PractRand, and, if we uncomment the <code>printf</code> statements, after 1 billion iterations we see that the outputs have not become uncorrelated. They continue to differ only in their high bit. And they will continue that way forever:</p>
<pre><code>61b0be0f
e1b0be0f
c5a003d8
45a003d8
20e14479
a0e14479
5a5ebe42
da5ebe42
99ce85af
19ce85af
d2a1aabb
52a1aabb
6bf29670
ebf29670
948587d6
148587d6
e2c0f91c
62c0f91c
536fe7eb
d36fe7eb
</code></pre>
<p>Similarly, Vigna's complaint about “strongly correlated very short sequences” could likewise be applied to XorWow. It consists of 2<sup>64</sup> very similar sequences (differing only by a constant). It might seem bad at a glance to concatenate a number of very similar sequences but it is worth realizing that the nearest similar sequence is 2<sup>128</sup>-1 steps away. If Vigna would characterize 2<sup>128</sup>-1 as “very short”, he must be using a mathematician's sense of scale.</p>
<p>Marsaglia's design of Xorwow quite deliberately uses a very simple and weak generator (a Weyl sequence) for a specific purpose. We could say “a counter isn't a very good random number generator”, but the key idea is that <em>it doesn't need to be</em>. It's not the whole story. It's a piece with a specific role to play, and it doesn't need to be any better than it is.</p>
<p>PCG's extended generation scheme is a similar story. The extension array is a funky counter akin to a Weyl sequence (each array element is like a digit of a counter). It's slightly better than a Weyl sequence (a single bit change will quickly affect all the bits in the in that array element), but it is essentially the same idea.</p>
<p>The <code>pcg64_k32_oneseq</code> and <code>pcg64_k32_fast</code> generators follow XorWow's scheme of just joining together the similar sequences. <code>pcg64_k32</code> swaps around chunks of size 2<sup>16</sup> from each similar sequence. In all cases, from any starting point you would need 2<sup>128</sup> outputs before the base linear congruential generator lined up to the same place again, and vastly more for the extension array to line up similarly. In short, for <code>pcg64_k32</code> the correlated states are quite literally unimaginably far away from each other.</p>
<p>Talking about his contrived seedings, Vigna notes that, “This is all the decorrelation we get after a billion iterations, and it will not improve (not significantly before the thermodynamical death of the universe).” What he seems to have missed is the corollary to his statements—correlation and decorrelation are sides of the same coin. Two currently <em>uncorrelated</em> <code>pcg64_k32</code> states will not correlate before the heat death of the universe either.</p>
<p>In short, Vigna contrived a seed to show correlation that would never arise in practice with normal seeding, nor could arise by advancing one generator. His critique is not unique to PCG, and should not be a concern for users of PCG.</p>
<h5 id="yes-and-response">“Yes, and…” Response</h5>
<p>A rather flippant “Yes, and…” response is that I'm perfectly happy for people to avoid <code>pcg64_k32</code>, as I'm not at all sure it is buying you anything meaningful over and above <code>pcg64</code>— it's a fair amount of added code complexity for something of dubious value. In fact, I didn't even bother to implement it in the C version and only a small number of people who have ported PCG have implemented it. As I see it, <em>k</em>-dimensional equidistribution sounds like a cool property, but the only use case I've found for such a property is <a href="http://www.pcg-random.org/party-tricks.html">performing party tricks</a>. But some people do like <em>k</em>-dimensional equidistribution, so let's press on…</p>
<p>First, Vigna went to far too much trouble to create correlated states. He copied the entire C++ source for PCG and hacked it to make a private data member public so he could set a single bit. Had he been more familiar with the features the extended generators provide, he could instead have written.</p>
<pre><code>pcg64_k32 rng0;
pcg64_k32 rng1 = rng0;
rng1.set(rng0() ^ 1);
</code></pre>
<p>This code uses <code>pcg64_k32</code>'s party-trick functionality to leap unimaginably huge distances across the state space to find exactly the correlated generator you want, one that is the same in every respect except for one differing output.</p>
<p>In other words, what he sees as a deficiency, I've already highlighted as a feature.</p>
<p>But whether it is achieved by the simple method above, or the more convoluted method Vigna used, we have the question of what to do if people are allowed to create very correlated generator states that would not normally arise in practice. One option is to just say “don't do that”, but a more “Yes, and…” perspective would be to allow people to create such states if they choose but provide a means to detect them. More on that in the next section.</p>
<p>It's also worth asking whether the slowness with which a single bit change diffuses across the extension array is something inherent in the design of PCG's extended generation scheme, or mere happenstance. In fact, it is the latter.</p>
<p>The only cleverness in the extended generation scheme isn't the idea of combining two generators, a strong one and a weaker-but-<em>k</em>-dimensionally-equidistributed one, it's the fact that we can do so without any <em>extra</em> state to keep track of what we're doing.</p>
<p>I'm thus not wedded to the particular Weyl-sequence inspired method I used. If it's important that unimaginably distant similar generators do not stay correlated for long, that's a very easy feature to provide.</p>
<p>When I designed how the extension array advances, I made a choice to make it “no better than it needs to be”. It doesn't need good avalanche properties, so that wasn't a design concern. But that doesn't mean it couldn't be tweaked to have good avalanche properties, so that a single bit change affects <em>all the bits</em> the next time the extension array advances. In fact, having designed <code>seed_seq_fe</code> for <code>randutils</code>, I'm aware of elegant and amply efficient ways to have better avalanche, so why not?</p>
<p>It may not really be <em>necessary</em>, but I actually like this idea. So thanks, Sebastiano, I'll address this issue in a future update to PCG that provides some alternative schemes for updating the extension array!</p>
<h4 id="pcg-regular-variants-contrived-seeds-for-inter-stream-correlations">PCG Regular Variants: Contrived seeds for Inter-Stream Correlations</h4>
<p>In his next concern, Vigna uses makes correlated generators from two “random looking” seeds. He presents a program, <a href="../downloads/snippets/corrpcg.c"><code>corrpcg.c</code></a> that mixes together the two correlated generators and can then be fed into statistical tests (which will fail because of the correlation).</p>
<h5 id="defensive-response_1">Defensive Response</h5>
<p>We can devise bad seed pairs for just about any PRNG. Here are three example programs, <a href="../downloads/snippets/corrxoshiro.c"><code>corrxoshiro.c</code></a>, <a href="../downloads/snippets/corrsplitmix.c"><code>corrsplitmix.c</code></a>, and <a href="../downloads/snippets/corrxorwow.c"><code>corrxorwow.c</code></a>, which initialize generators with two “random looking” seeds but create correlated streams that will fail statistical tests if mixed.</p>
<p>In all cases, despite being “random looking”, the seeds are carefully contrived. Seeds such as these would be vanishingly unlikely with proper seeding practice.</p>
<p>As before, the concerns Vigna expresses apply to many prior generators. We can view XorWow's <code>state[4]</code> value as being a stream selection constant, but this time let's focus in on SplitMix. For SplitMix, different <code>gamma_</code> values constitute different streams.</p>
<p>In <code>corrsplitmix.c</code> the implementation is hard-wired to use a single stream (<code>0x9e3779b97f4a7c15</code>), but in <a href="../downloads/snippets/corrsplitmix2.c"><code>corrsplitmix2.c</code></a> we mix two streams, (<code>0x9e3779b97f4a7c15</code> and <code>0xdf67d33dd518d407</code>) and observe correlations. Although these gamma values look random, they are not, they are carefully contrived. In particular, here <code>0xdf67d33dd518d407 * 3</code> = <code>0x9e3779b97f4a7c15</code> (in 64-bit arithmetic), which means that every third output from the second stream will exactly match an output from the first.</p>
<p>Vigna's critique thus applies at least as strongly to SplitMix's streams as it does to PCG's.</p>
<p>I have <a href="critiquing-pcg-streams.html">written at length about PCG's streams</a> (and discussed SplitMix's, too). I freely acknowledge that these streams exist in a space of trade-offs where we are choosing to do the cheap thing, leveraging the properties of the underlying LCG (or Weyl sequence for SplitMix). In that article, I say:</p>
<blockquote>
<p>Changing the increment parameter is just barely enough for streams that are actually useful. They aren't statistically independent, far from it, but they are distinct and they do help.</p>
</blockquote>
<p>No one should worry that PCG's streams makes anything worse.</p>
<h5 id="yes-and-response_1">“Yes, and…” Response</h5>
<p>Although it is vanishingly unlikely that two randomly seeded <code>pcg64</code> generators would be correlated (it would only happen with poor/adversarial seeding), it is reasonable to ask if this kind of correlation due to bad seeding can be detected.</p>
<p>We can even argue that another <em>checklist feature</em> for a general-purpose PRNG is the ability to tell how independent the sequences from two seeds are likely to be. PCG goes some way towards this goal with its <code>-</code> operator that calculates the distance between two generators, but the functionality was originally designed for generators on the same stream. I've now updated that functionality so that for generators on different streams, it will calculate the distance to their point of closest approach (i.e., where the differences between successive values of the underlying LCG align).</p>
<p>So it's now possible with PCG to compare two generators to see whether they have been badly seeded so that they correlate.</p>
<p>Here's a short <a href="../downloads/snippets/strmdist.c">test program</a>:</p>
<pre><code>#include "pcg_random.hpp"
#include "pcg_extras.hpp"
#include &lt;iostream&gt;
#include &lt;iomanip&gt;
#include &lt;random&gt;
int main() {
using namespace pcg_extras;
#if USE_VIGNA_CONTRIVED_SEEDS
pcg64 x(PCG_128BIT_CONSTANT(0x83EED115C9CBCC30, 0x4C55E45838B75647),
PCG_128BIT_CONSTANT(0x3E0897751B1A19E7, 0xD9D50DD3E3A454DC));
pcg64 y(PCG_128BIT_CONSTANT(0x7C112EEA363433CF, 0xB3AA1BA7C748A9B9),
PCG_128BIT_CONSTANT(0x41F7688AE4E5E618, 0x262AF22C1C5BAB23));
#elif USE_PCG_UNIQUE
pcg64_unique x,y;
#elif USE_SMALL_SEEDS1
pcg64 x(0), y(1);
#elif USE_SMALL_SEEDS2
pcg64 x(0,0), y(0,1);
#elif USE_SMALL_SEEDS3
pcg64 x(0,0), y(1,1);
#elif USE_RANDOM_DEVICE
pcg64 x(seed_seq_from&lt;std::random_device&gt;{}),
y(seed_seq_from&lt;std::random_device&gt;{});
#endif
std::cout &lt;&lt; std::hex;
for (int i = 0; i &lt; 10; ++i) {
std::cout &lt;&lt; (x - y) &lt;&lt; ": ";
std::cout &lt;&lt; x() &lt;&lt; ", " &lt;&lt; y() &lt;&lt; "\n";
}
}
</code></pre>
<p>And here are the results of running it (in each case, each line shows the distance between the streams and a value from each PRNG; the distance stays the same because the PRNGs are advancing together):</p>
<pre><code>unix% c++ -Wall -std=c++11 -o strmdist strmdist.cpp -Iinclude -DUSE_RANDOM_DEVICE &amp;&amp; ./strmdist
a571d615b08fea47c84f39f0811f04f: c021049beac5efd0, ceaa3596f168e8b6
a571d615b08fea47c84f39f0811f04f: 573371998db59a67, e5d84a00b37c3556
a571d615b08fea47c84f39f0811f04f: bc4246c671ef9a1f, 1b13ad2f224707c7
a571d615b08fea47c84f39f0811f04f: b1f3e4ffcfef569, 11b50b226a67cdbe
a571d615b08fea47c84f39f0811f04f: 8a378ec693dc1e4, 903ccfd4dc769389
a571d615b08fea47c84f39f0811f04f: 4799de5c580be6ab, 22d13ce52d83c9cb
a571d615b08fea47c84f39f0811f04f: e8fdf041a93626e8, f24c8f49866b7b4e
a571d615b08fea47c84f39f0811f04f: f29e3d08104d7630, b37e5b58ae91d45c
a571d615b08fea47c84f39f0811f04f: 28f524ad8f57bedb, 52d41d39b1186616
a571d615b08fea47c84f39f0811f04f: 9be8cb37ea8952b5, e6812ed8f0613d3
unix% c++ -Wall -std=c++11 -o strmdist strmdist.cpp -Iinclude -DUSE_RANDOM_DEVICE &amp;&amp; ./strmdist
25c3990ef6e7766ab543435aa25f4326: 2f76ab68249fd7f5, 4fbfc0ce19119391
25c3990ef6e7766ab543435aa25f4326: 933845d6c7ad9396, 7572dae64b2cc5a
25c3990ef6e7766ab543435aa25f4326: d7d1dc18bae0604a, 5b1f8310e1f0dc8a
25c3990ef6e7766ab543435aa25f4326: 85cd1dcff8830ad5, a1cfea3c01314c8d
25c3990ef6e7766ab543435aa25f4326: 543ba46266a0b6ba, 7217b15c05cba254
25c3990ef6e7766ab543435aa25f4326: 5a3bd5d4d6c49a55, a243af7df5cfe287
25c3990ef6e7766ab543435aa25f4326: 9f2dc30afc3dcead, deaa9d03f7ca1117
25c3990ef6e7766ab543435aa25f4326: 5856b884c1298dc9, 67502e4490b77bae
25c3990ef6e7766ab543435aa25f4326: 9b94ebb084cc6fdd, 2e07957697add77c
25c3990ef6e7766ab543435aa25f4326: efe6b451c262a3fb, 2e94d782daae964d
unix% c++ -Wall -std=c++11 -o strmdist strmdist.cpp -Iinclude -DUSE_RANDOM_DEVICE &amp;&amp; ./strmdist
32982840d1ddcb5e7f1ed57a6d496525: 96ed26957ef938db, 568fe0aa7e9e8a26
32982840d1ddcb5e7f1ed57a6d496525: 33270d80d24b0965, 44e42e1afc4db710
32982840d1ddcb5e7f1ed57a6d496525: 6de9ac5272dd1193, 90696d1c4f52e71d
32982840d1ddcb5e7f1ed57a6d496525: 43c5c899c7123e57, 337b9d25e00fb0de
32982840d1ddcb5e7f1ed57a6d496525: 753954b73076704d, f4fce4c33756df7e
32982840d1ddcb5e7f1ed57a6d496525: 3b5dc9402b56584d, fd7ae3c708355dc0
32982840d1ddcb5e7f1ed57a6d496525: 15a9227305a442d8, 78fa04eb7f881590
32982840d1ddcb5e7f1ed57a6d496525: b9e58872c3a299, 381a8f851acbc5f4
32982840d1ddcb5e7f1ed57a6d496525: 1b624879e6cf5128, aa908d3a4f2d8f02
32982840d1ddcb5e7f1ed57a6d496525: 79d4836bb5a56a77, 1650f74b3ef617f9
unix% c++ -Wall -std=c++11 -o strmdist strmdist.cpp -Iinclude -DUSE_SMALL_SEEDS1 &amp;&amp; ./strmdist
1c31b969dc65d7b0df636de659042bb1: 1070196e695f8f1, e175e32ed3507bfa
1c31b969dc65d7b0df636de659042bb1: 703ec840c59f4493, c0bf922a0b283109
1c31b969dc65d7b0df636de659042bb1: e54954914b3a44fa, 140bfa21e68785bb
1c31b969dc65d7b0df636de659042bb1: 96130ff204b9285e, c5ec8bcc4fe35830
1c31b969dc65d7b0df636de659042bb1: 7d9fdef535ceb21a, 4dd8ed1ca22869c5
1c31b969dc65d7b0df636de659042bb1: 666feed42e1219a0, c9bffa29c802ef4c
1c31b969dc65d7b0df636de659042bb1: 981f685721c8326f, 3aa09aa4e147478b
1c31b969dc65d7b0df636de659042bb1: ad80710d6eab4dda, 1dfdf6222d06378c
1c31b969dc65d7b0df636de659042bb1: e202c480b037a029, 5a05dacf4df61d4e
1c31b969dc65d7b0df636de659042bb1: 5d3390eaedd907e2, 489650b1eb840a26
unix% c++ -Wall -std=c++11 -o strmdist strmdist.cpp -Iinclude -DUSE_SMALL_SEEDS2 &amp;&amp; ./strmdist
151361a7e7368c239a3988178df4d76d: d4feb4e5a4bcfe09, acdbf879b3c73375
151361a7e7368c239a3988178df4d76d: e85a7fe071b026e6, 7ea754d074e8d88f
151361a7e7368c239a3988178df4d76d: 3a5b9037fe928c11, f8fc7aec8ae6245a
151361a7e7368c239a3988178df4d76d: 7b044380d100f216, 7d2ebc3c0b5bedb4
151361a7e7368c239a3988178df4d76d: 1c7850a6b6d83e6a, cbaf666f55051666
151361a7e7368c239a3988178df4d76d: 240b82fcc04f0926, 4eba9f04dfb9903b
151361a7e7368c239a3988178df4d76d: 7e43df85bf9fba26, 4fab6bcf361bd63d
151361a7e7368c239a3988178df4d76d: 43adf3380b1fe129, 257fcac1ed3817df
151361a7e7368c239a3988178df4d76d: 3f0fb307287219c, bf6f5515988a494
151361a7e7368c239a3988178df4d76d: 781f4b84f42a2df, 1081ed38c84c1c9d
unix% c++ -Wall -std=c++11 -o strmdist strmdist.cpp -Iinclude -DUSE_SMALL_SEEDS3 &amp;&amp; ./strmdist
edfe668df810de6e58b8e92e878fefa: d4feb4e5a4bcfe09, d4692f845d3a3706
edfe668df810de6e58b8e92e878fefa: e85a7fe071b026e6, bb0f09b0eebab6ff
edfe668df810de6e58b8e92e878fefa: 3a5b9037fe928c11, e26ac904ad283c09
edfe668df810de6e58b8e92e878fefa: 7b044380d100f216, 83860212b5d92197
edfe668df810de6e58b8e92e878fefa: 1c7850a6b6d83e6a, 1c3601ed5afd3f49
edfe668df810de6e58b8e92e878fefa: 240b82fcc04f0926, 5e4fa027be29b47e
edfe668df810de6e58b8e92e878fefa: 7e43df85bf9fba26, b930e28d59383019
edfe668df810de6e58b8e92e878fefa: 43adf3380b1fe129, e0d61e1b074df835
edfe668df810de6e58b8e92e878fefa: 3f0fb307287219c, f42c38b1aca3ac9d
edfe668df810de6e58b8e92e878fefa: 781f4b84f42a2df, 19e9cc4fa58fd0ad
unix% c++ -Wall -std=c++11 -o strmdist strmdist.cpp -Iinclude -DUSE_PCG_UNIQUE &amp;&amp; ./strmdist
534a7c98f86b50b72fad6990038ba18: af8a07de4c8d67d1, d649257470c0180d
534a7c98f86b50b72fad6990038ba18: 3789d12fe8e452b1, 1017152e85f732fc
534a7c98f86b50b72fad6990038ba18: c3c4e780fd60901b, 91a9d78551f0c776
534a7c98f86b50b72fad6990038ba18: e7257e02f7fa5b40, 46fb62417ebf2f13
534a7c98f86b50b72fad6990038ba18: 3697948fa9aa8378, 60e44721c6fbc9d0
534a7c98f86b50b72fad6990038ba18: 7bdbcc91de7efbcf, 21de9d1dc03e2ca6
534a7c98f86b50b72fad6990038ba18: 9cf598a61c9ad958, 62e8c3dc421f4e58
534a7c98f86b50b72fad6990038ba18: 5c8a6da6c91b7d35, 3cb08b7e59fd655a
534a7c98f86b50b72fad6990038ba18: f55a8b190a85c9c0, 5a71766fac52ec8a
534a7c98f86b50b72fad6990038ba18: 906b1a30904fe59, f71525dc1d91a06e
unix% c++ -Wall -std=c++11 -o strmdist strmdist.cpp -Iinclude -DUSE_PCG_UNIQUE &amp;&amp; ./strmdist
1b7a9a85b5ed2b6a2a92da9e093eba18: a11d6aa92efc9a79, e646943445e368a
1b7a9a85b5ed2b6a2a92da9e093eba18: 35026a6e1a195a29, 906b9bed756e1667
1b7a9a85b5ed2b6a2a92da9e093eba18: af1f1193515d9e7b, fe51967d5d532f70
1b7a9a85b5ed2b6a2a92da9e093eba18: 61baa5620ceeff38, 644345c453ee3b11
1b7a9a85b5ed2b6a2a92da9e093eba18: 71e88c9c27a7abbf, 1b6a254f565f6c70
1b7a9a85b5ed2b6a2a92da9e093eba18: 1125753cd420e3c1, 8be4065858e93c57
1b7a9a85b5ed2b6a2a92da9e093eba18: a53ce57ffaa57eb3, 7f1c546ae9bf7b61
1b7a9a85b5ed2b6a2a92da9e093eba18: 4cf2c7c152326c4, ada2d31650f07ef8
1b7a9a85b5ed2b6a2a92da9e093eba18: b731cbec3bfba773, 92ce80f0c8dc855f
1b7a9a85b5ed2b6a2a92da9e093eba18: b8c449d4872f7971, 44ed4207442550da
unix% c++ -Wall -std=c++11 -o strmdist strmdist.cpp -Iinclude -DUSE_PCG_UNIQUE &amp;&amp; ./strmdist
360981a27aee6d34271feaa80270ba18: 5da8c0afa4330059, 67af26ab1d05ed52
360981a27aee6d34271feaa80270ba18: ef0ef074871cc9a0, cda2688372cb72b7
360981a27aee6d34271feaa80270ba18: 6a15c49d4ae8d89d, 3708ddd964f616fe
360981a27aee6d34271feaa80270ba18: dd8f24112bcbf580, 69309c3ffa6cea2e
360981a27aee6d34271feaa80270ba18: e8f252a4132fd0e3, e3ff9751773f6db
360981a27aee6d34271feaa80270ba18: e23a1246ea5980be, 1161fd499cbecafa
360981a27aee6d34271feaa80270ba18: 1d19a64904134065, a9e31a01b4c51a43
360981a27aee6d34271feaa80270ba18: 2c3166d304f9dedf, fdd3f540a6859c19
360981a27aee6d34271feaa80270ba18: 8f73778d1f6133ea, 13a54957b3c65205
360981a27aee6d34271feaa80270ba18: c8d362ba3d62239, 66db0b2ae6908dc8
unix% c++ -Wall -std=c++11 -o strmdist strmdist.cpp -Iinclude -DUSE_PCG_UNIQUE &amp;&amp; ./strmdist
1266069359d4404d4fe77f291da43a18: 9994872b3cc3104c, 5582722b3f354f4b
1266069359d4404d4fe77f291da43a18: cec9ae92f2f0a929, 7a2d534e7c3a7281
1266069359d4404d4fe77f291da43a18: ce777879518e6169, c384bb65c1d4364b
1266069359d4404d4fe77f291da43a18: 2cb082454d09aa19, 703c5ad7747a9b42
1266069359d4404d4fe77f291da43a18: a581d3154c60654, b4b9369d997cda6e
1266069359d4404d4fe77f291da43a18: 5ba66e3d99cd33c9, 80aa887fbb5fdef3
1266069359d4404d4fe77f291da43a18: 1038e3281dcae11d, 54c304cf2a66182c
1266069359d4404d4fe77f291da43a18: 9df3df9d27af7148, 7ddd385e114299b9
1266069359d4404d4fe77f291da43a18: bf1656198867bd08, 7aeae9ba84a17dbe
1266069359d4404d4fe77f291da43a18: 60aef1418aa1c6f1, 8a7196feda932f06
unix% c++ -Wall -std=c++11 -o strmdist strmdist.cpp -Iinclude -DUSE_VIGNA_CONTRIVED_SEEDS &amp;&amp; ./strmdist
0: e1e4e4b44cca9ade, 43dc3c9c96899953
0: a3ef563648055140, 2b8a051f7ab1b24
0: 7aa3dc341221459a, 1a0960a2cd3d51ee
0: cfa0d055fbe9f476, a0abf5d3e8ed9f41
0: b69403f2c93f3fce, 807e58a7e7f9d6d2
0: a2550ed76e8d9ae, 144aa1daedd1b35e
0: a1f898a64347533b, c532263a99dd0fc4
0: d483377a20c295f0, bbd10614af86a019
0: 5c6469b1053d2ce1, 9c2b8c8d2e20a7a5
0: 5f91b4bd64d5eeb1, 58afc8da4eb26af7
</code></pre>
<p>As we can see in the last example, Vigna contrived seeds that had the streams exactly aligned. The values from each stream are distinct in this case, but a statistical test will see that they are correlated.</p>
<p>Interpreting the distance value is easy in this case, but not every user will be able to do so, and some distances (e.g., a just a single high bit set) would also be bad, so better detection of contrived seeds probably demands a new function, <code>independence_score()</code>, based on this distance metric.</p>
<p>Beyond these functions, there is also the question of whether it is wise to allow users to seed generators where they can specify the entire internal state. Vigna's generators (and all basically LFSR generators) must avoid the all-zeros state and do not like states with low hamming weight (so <code>{ seed, 0, 0, 0 }</code> is also a poor choice). With these issues in mind, perhaps we should deny users the ability to seed the entire state. That might prevent some contrived seedings like the one Vigna used. I'm not fully sold on this idea, but it is a widely-used approach use by other generators (e.g., Blackman's gjrand) and worth considering.</p>
<p>Although Vigna's contrived seeding was a bit silly, his example has helped me improve the PCG distance metric, given us another checklist feature that some people might want (detecting bad seed pairs), got me thinking about future features, and returned me to the topic of good seeding. All in all, we can call this a positive contribution. Thanks, Sebastiano!</p>
<h3 id="prediction-difficulty">Prediction Difficulty</h3>
<p>The next two sections relate to predicting PCG.</p>
<h4 id="predicting-pcg32_oneseq">Predicting <code>pcg32_oneseq</code>
</h4>
<p>Vigna writes:</p>
<blockquote>
<p>To me, it has been always evident that PCG generators are very easy to predict. Of course, no security expert ever tried to to that: it would be like beating 5-year-old kid on a race. It would be embarrassing.</p>
<p>So we had this weird chicken-and-egg situation: nobody who could easily predict a PCG generator would write the code, because they are too easy to predict; but since nobody was predicting a PCG generator, Melissa O'Neill kept on the absurd claim that they were challenging to predict.</p>
</blockquote>
<p>Vigna then goes on to show code to predict <code>pcg32_oneseq</code>, a 64-bit PRNG with 32-bit output.</p>
<h5 id="defensive-response_2">Defensive Response</h5>
<p>As one reddit observer wrote:</p>
<blockquote>
<p>[Vigna's] program needs to totally brute force half of the state, and then some additional overhead to brute force bits of the rest of the state, so runtime is 2n/2, exponential, not polynomial.</p>
</blockquote>
<p>Vigna has written an exponential algorithm to brute force 32 bits of state. I hope it was obvious to almost everyone that I never claimed that brute-forcing 32-bits of state was hard. In fact, I have already <a href="http://www.pcg-random.org/predictability.html#predictability-of-the-pcg-family">outlined how</a> to predict <code>pcg32</code> (more bits to figure out given the unknown stream). I observed that <code>pcg32</code> is predictable using established techniques (specifically the LLL algorithm), and I have even linked to <a href="https://github.com/mariuslp/PCG_attack">an implementation</a> of those ideas by Marius Lombard-Platet.</p>
<p>I characterize <code>pcg32_oneseq</code> as easy to brute force, and <code>pcg32</code> as annoying (as Marius Lombard-Platet discovered). Only when we get to <code>pcg64</code> do we have something where there is a meaningful challenge.</p>
<p>If Vigna really believes that <em>all</em> members of the PCG family are easy to predict, he should have predicted <code>pcg64</code> or <code>pcg64_c32</code>.</p>
<h5 id="yes-and-response_2">“Yes, and…” Response</h5>
<p>The best part of Vigna's critique are these lines:</p>
<blockquote>
<p>Writing the function that performs the prediction, <code>recover()</code>, took maybe half an hour of effort. It's a couple of loops, a couple of if's and a few logical operations. Less than 10 lines of code (of course this can be improved, made faster, etc.).</p>
</blockquote>
<p>and the source code comment that reads:</p>
<blockquote>
<p>Pass an initial state (in decimal or hexadecimal), see it recovered from the output in a few seconds.</p>
</blockquote>
<p>So, here Vigna is essentially endorsing all the <em>practical</em> aspects I've previously noted regarding trivial predictability. Specifically, he's noting that with little <em>time</em> or <em>effort</em>, he can write a <em>simple</em> program that <em>quickly</em> predicts a PRNG and has actually done so. This is very different from taking a purely theoretical perspective (e.g., noting that techniques exist to solve a problem in polynomial time without ever implementing them).</p>
<p>In other words, clearly <em>ease of prediction</em> matters to Vigna. So we both <em>agree</em><code>pcg32_oneseq</code> is easy to predict.</p>
<p>Now let's keep that characterization of easiness and move on to some of the other generators.</p>
<p>Vigna and I would agree, I think, that <em>I</em> lack the necessary insight to develop fast prediction methods for <code>pcg64</code> or <code>pcg64_c32</code> (it's an instance of <a href="https://www.schneier.com/blog/archives/2011/04/schneiers_law.html">Schneier's Law</a>). Vigna is also right that, if it is tractable to predict, those who might have the necessary skill lack much incentive to try. For some years I have been intending to have a prediction contest with real prizes and I remain hopeful that I'll find the time to run such a contest this summer. When the contest finally launches, I hope he'll have a go—I'd be delighted to send him a prize.</p>
<h4 id="predicting-pcg64_once_insecure">Predicting <code>pcg64_once_insecure</code>
</h4>
<p>Vigna also notes that he can invert the bijection that serves as the output function for <code>pcg64_once_insecure</code>, which reveals the underlying LCG with all its statistical flaws.</p>
<h5 id="defensive-response_3">Defensive Response</h5>
<p>I noted this exact issue in 2014 in the PCG paper. It's why <code>pcg64_once_insecure</code> has the name it does. I discourage its use as a general-purpose PRNG precisely because of its invertible output function.</p>
<h5 id="yes-and-response_3">“Yes, and…” Response</h5>
<p>Vigna is at least acknowledging that some people might care about this property.</p>
<h3 id="speed-and-comparison-against-lcgs">Speed and Comparison against LCGs</h3>
<p>Finally, Vigna develops a PCG variant using a traditional integer hash function based on MurmurHash (I would call it PCG XS M XS M XS). He claims it is faster than the PCG variants I recommend and notes that he doesn't consider PCG especially fast.</p>
<h4 id="defensive-response_4">Defensive Response</h4>
<p>I considered this exact idea in the 2014 PCG paper. In my tests, I found that a variant using a very similar general integer hash function was not as fast as the PCG permutations I used.</p>
<p>Testing is a finicky business.</p>
<h4 id="yes-and-response_4">“Yes, and…” Response</h4>
<p>I absolutely agree with Vigna's claim that people should run their own speed tests.</p>
<p>I also realized long ago that PCG probably won't have the speed crown, because it can't. A simple truncated 128-bit LCG passes all standard statistical tests once we get up to 128 bits, and beats everything, including Vigna's generators. Because <code>pcg64</code> is built from a 128-bit LCG, it can never beat it in speed.</p>
<p>I should write a blog post on speed testing. But here's a taste. We'll use Vigna's hamming-weight test as our benchmark, because it is a real program that puts randomness to actual use but is coded with execution speed in mind.</p>
<p>First, let's test the Mersenne Twister. Compiling with Clang, we get</p>
<pre><code>processed 1.75e+11 bytes in 130 seconds (1.346 GB/s, 4.847 TB/h). Fri May 25 14:03:25 2018
</code></pre>
<p>whereas compiling with GCC, we get</p>
<pre><code>processed 1.75e+11 bytes in 73 seconds (2.397 GB/s, 8.631 TB/h). Fri May 25 14:05:44 2018
</code></pre>
<p>With GCC, it runs almost twice as fast.</p>
<p>Now let's contrast that result with this 128-bit MCG:</p>
<pre><code>static uint128_t state = 1; // can be seeded to any odd number
static inline uint64_t next()
{
constexpr uint128_t MULTIPLIER =
(uint128_t(0x0fc94e3bf4e9ab32ULL) &lt;&lt; 64) | 0x866458cd56f5e605ULL;
// Spectral test: M8 = 0.71005, M16 = 0.66094, M24 = 0.61455
state *= MULTIPLIER;
return state &gt;&gt; 64;
}
</code></pre>
<p>Compiling with Clang, we get</p>
<pre><code>processed 1.75e+11 bytes in 39 seconds (4.488 GB/s, 16.16 TB/h). Fri May 25 14:16:25 2018
</code></pre>
<p>whereas with GCC we get</p>
<pre><code>processed 1.75e+11 bytes in 58 seconds (3.017 GB/s, 10.86 TB/h). Fri May 25 14:18:14 2018
</code></pre>
<p>The GCC code is no slouch, but Clang's code here is <em>much</em> faster. Clang is apparently better at 128-bit math.</p>
<p>If we really care about speed though, <em>this</em> 128-bit MCG (which uses a carefully chosen 64-bit multiplier instead of a more typical 128-bit multiplier) is even faster and still passes statistical tests:</p>
<pre><code>static uint128_t state = 1; // can be seeded to any odd number
static inline uint64_t next()
{
return (state *= 0xda942042e4dd58b5ULL) &gt;&gt; 64;
}
</code></pre>
<p>Compiling with Clang, we get</p>
<pre><code>processed 1.75e+11 bytes in 37 seconds (4.73 GB/s, 17.03 TB/h). Fri May 25 14:09:26 2018
</code></pre>
<p>whereas with GCC we get</p>
<pre><code>processed 1.75e+11 bytes in 44 seconds (3.978 GB/s, 14.32 TB/h). Fri May 25 14:11:40 2018
</code></pre>
<p>Again, Clang takes the speed crown; its executable generates and checks 1 TB of randomness about every 3.5 minutes.</p>
<p>If we test Vigna's latest generator, xoshiro256**, and compile with Clang, it gives us</p>
<pre><code>processed 1.75e+11 bytes in 50 seconds (3.5 GB/s, 12.6 TB/h). Fri May 25 14:30:05 2018
</code></pre>
<p>whereas with GCC we get</p>
<pre><code>processed 1.75e+11 bytes in 43 seconds (4.07 GB/s, 14.65 TB/h). Fri May 25 14:31:52 2018
</code></pre>
<p>This result is very fast, but not faster than either 128-bit MCG.</p>
<p>Finally, let's look at PCG-style generators. First let's look at Vigna's proposed variant. Compiling with Clang, we get</p>
<pre><code>processed 1.75e+11 bytes in 59 seconds (2.966 GB/s, 10.68 TB/h). Fri May 25 14:44:37 2018
</code></pre>
<p>and with GCC we get</p>
<pre><code>processed 1.75e+11 bytes in 62 seconds (2.823 GB/s, 10.16 TB/h). Fri May 25 14:46:42 2018
</code></pre>
<p>This is one of the rare occasions where GCC and Clang actually turn in almost equivalent times.</p>
<p>In contrast, with the general-purpose <code>pcg64</code> generator, compiling with Clang I see:</p>
<pre><code>processed 1.75e+11 bytes in 57 seconds (3.07 GB/s, 11.05 TB/h). Fri May 25 14:57:02 2018
</code></pre>
<p>whereas with GCC, I see</p>
<pre><code>processed 1.75e+11 bytes in 64 seconds (2.735 GB/s, 9.844 TB/h). Fri May 25 14:59:07 2018
</code></pre>
<p>Thus, depending on which compiler we choose, Vigna's variant is either slightly faster or slightly slower.</p>
<p>Finally, if we look at <code>pcg64_fast</code>, compiling with Clang gives us</p>
<pre><code>processed 1.75e+11 bytes in 49 seconds (3.572 GB/s, 12.86 TB/h). Fri May 25 15:00:45 2018
</code></pre>
<p>and with GCC we get</p>
<pre><code>processed 1.75e+11 bytes in 65 seconds (2.693 GB/s, 9.693 TB/h). Fri May 25 15:02:15 2018
</code></pre>
<p>Again the performance of GCC is a bit disappointing; this MCG-based generator is actually running slower than the LCG-based one.</p>
<p>From this small amount of testing, we can see that <code>pcg64</code> is not as fast as <code>xoshiro256**</code>, but a lot depends on the compiler you're using—if you're using Clang (which is the default compiler on OS X), <code>pcg64_fast</code> will beat xoshiro256**.</p>
<p>There's plenty of room for speed improvement in PCG. My original goal was to be faster than the Mersenne Twister, which it is, but knowing that it'll always be beaten by the speed of its underlying LCG I haven't put a lot of effort into optimizing the code. I could have used the faster multiplier that I used above, and there is actually a completely different way of handling LCG increment that reduces dependences and enhances speed but implementing LCGs that way makes the code more opaque. If PCG's speed is an issue, these are design decisions are worth revisiting.</p>
<p>But the speed winner is clearly a 128-bit MCG. It's actually what I use when speed is the primary criterion.</p>
<h3 id="conclusion">Conclusion</h3>
<p>None of Vigna's concerns raise any serious worries about PCG. But critique is useful, and helps spur us to do better.</p>
<p>I'm sure Vigna has spent far longer thinking about PCG than he would like, so it is best to say a big thank you to him for all the thought and energy he has expended in these efforts. I'm pleased that I've mostly been able to put the critique to good use—it may be mostly specious for users, but it is certainly helpful for me. Reddit mostly saw vitriol and condescension, but I prefer to see it as a gift of his time and thought.</p>
<p>Thanks, Sebastiano!</p>
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19
references/refs.txt Normal file
View File

@ -0,0 +1,19 @@
$ cat squiggle.c | grep http | sed 's|.*http|http|g'
https://en.wikipedia.org/wiki/Xorshift
https://stackoverflow.com/questions/53886131/how-does-xorshift32-works
https://www.pcg-random.org/posts/on-vignas-pcg-critique.html
https://prng.di.unimi.it/
https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform
https://stackoverflow.com/questions/20626994/how-to-calculate-the-inverse-of-the-normal-cumulative-distribution-function-in-p
https://www.wolframalpha.com/input?i=N%5BInverseCDF%28normal%280%2C1%29%2C+0.05%29%2C%7B%E2%88%9E%2C100%7D%5D
https://en.wikipedia.org/wiki/Normal_distribution?lang=en#Operations_on_a_single_normal_variable
https://dl.acm.org/doi/pdf/10.1145/358407.358414
https://en.wikipedia.org/wiki/Gamma_distribution
https://dl.acm.org/doi/pdf/10.1145/358407.358414
https://en.wikipedia.org/wiki/Gamma_distribution#Related_distributions
https://en.wikipedia.org/wiki/Beta_distribution?lang=en#Rule_of_succession
$ cat squiggle_more.c | grep http | sed 's|.*http|http|g'
https://en.wikipedia.org/wiki/Quickselect