As I commented on reddit;<p>I&#x27;ve not read much, but the start where it says<p>&gt; There are two approaches to parallelizing a PRNG—multistream and substream—but both approaches have practical difficulties for conventional PRNGs.<p>just screams &quot;use PCG random!&quot; to me. PCG random numbers support good, independent random streams really easily and also support substream partitioning. These don&#x27;t require any challenging seeding, and they&#x27;re some of the fastest RNGs out there. They&#x27;re also small - 64 bits for a 64 bit period, 128 for 128 bit period, etc. Probably double that if you need streams, unless you&#x27;re using their address for stream splitting.<p>&#x27;Course, there was no option for this in 2011.<p>Also, I&#x27;d be interested to know if the PCG test in Figures 7 and 15 of the paper[1] can be applied to this generator and, if so, how it fares. It&#x27;s the only test I know of that isn&#x27;t pass-fail, so helps give extra context on how strong an RNG <i>actually</i> is.<p>Also, where it&#x27;s said<p>&gt; On the other hand, many of the most widely used PRNGs fail quite dramatically, including all of the linear congruential generators<p>do note that a 92 bit LCG outputting 32 bit integers, and a 128 bit LCG outputting 64 bit integers <i>do</i> in fact pass the statistical tests. LCGs are, in many ways, actually pretty good with larger state sizes. Not that implementers will take note :P.<p>[1] <a href="http:&#x2F;&#x2F;www.pcg-random.org&#x2F;pdf&#x2F;toms-oneill-pcg-family-v1.02.pdf" rel="nofollow">http:&#x2F;&#x2F;www.pcg-random.org&#x2F;pdf&#x2F;toms-oneill-pcg-family-v1.02.p...</a>