<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>Random-Number-Generation on Pi Stack</title>
    <link>https://www.pistack.xyz/tags/random-number-generation/</link>
    <description>Recent content in Random-Number-Generation on Pi Stack</description>
    <generator>Hugo</generator>
    <language>en-us</language>
    <lastBuildDate>Fri, 19 Jun 2026 00:00:00 +0000</lastBuildDate>
    <atom:link href="https://www.pistack.xyz/tags/random-number-generation/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>Open-Source PRNG Algorithm Libraries: Xoshiro vs PCG vs SplitMix vs Mersenne Twister</title>
      <link>https://www.pistack.xyz/posts/2026-06-19-prng-algorithm-libraries-xoshiro-pcg-splitmix-mersenne-twister/</link>
      <pubDate>Fri, 19 Jun 2026 00:00:00 +0000</pubDate>
      <guid>https://www.pistack.xyz/posts/2026-06-19-prng-algorithm-libraries-xoshiro-pcg-splitmix-mersenne-twister/</guid>
      <description>&lt;h2 id=&#34;introduction&#34;&gt;Introduction&lt;/h2&gt;&#xA;&lt;p&gt;Random number generation is foundational to scientific computing, game development, cryptography, and statistical simulation. While your operating system provides &lt;code&gt;/dev/urandom&lt;/code&gt; and language runtimes ship with default generators, the quality, speed, and statistical properties of those defaults vary dramatically. For Monte Carlo simulations, procedural content generation, randomized algorithms, and reproducible research, choosing the right &lt;strong&gt;pseudo-random number generator (PRNG)&lt;/strong&gt; algorithm is critical.&lt;/p&gt;</description>
    </item>
  </channel>
</rss>
