[rramadass]

You need to understand PRNGs to answer that question. It is complicated and nothing to do with C++ language itself.

Here is cppreference on PRNGs (note the various engines available) - https://www.cppreference.com/w/cpp/numeric/random.html You have to "know" how to combine the various options available to get an optimal sequence.

The Mersenne Twister (MT) was one of the best engines and was the default in many other languages/packages too. See "Applications" section in wikipedia - https://en.wikipedia.org/wiki/Mersenne_Twister

The author identified distribution problems with the 32-bit versions of MT (i am not sure whether similar problems exist with its 64-bit versions) and proposed a different one named "Permuted Congruential Generator (PCG)" which has now been adopted as the default by many of the languages/packages - https://en.wikipedia.org/wiki/Permuted_congruential_generato...

As you can now appreciate, the subject is mathematically complicated and the defaults chosen by the language/package implementer becomes the "most commonly used" and hence reference case. While this is good enough for most "normal" applications if you are doing any special simulations (Monte Carlo or otherwise) and/or specific Numerical Computations it is your responsibility to understand what it is that you need and program accordingly using the various options (if available) or roll your own.

[o11c]

> (i am not sure whether similar problems exist with its 64-bit versions) and proposed a different one named "Permuted Congruential Generator (PCG)"

The 64-bit version might be a bit faster (for certain workloads, on 64-bit hardware) than the 32-bit version, but still wastes the same space and has the same mathematical flaws.

PCG is still not perfect (128-bit math hurts, though the new DXSM variant at least reduces that to 128x64), but its mathematical properties are nicer than the xor* family (its main competitor), and both families are miles ahead of any other RNG out there.

[rramadass]

It is understanding the non-trivial statistical properties (even at a simple conceptual level) that is of paramount importance. PRNG is one of the most difficult subjects in Numerical Computation and has nothing whatever to do with any language/package/library etc.

From https://en.wikipedia.org/wiki/Pseudorandom_number_generator

Even today, caution is sometimes required, as illustrated by the following warning in the International Encyclopedia of Statistical Science (2010).

The list of widely used generators that should be discarded is much longer [than the list of good generators]. Do not trust blindly the software vendors. Check the default RNG of your favorite software and be ready to replace it if needed. This last recommendation has been made over and over again over the past 40 years. Perhaps amazingly, it remains as relevant today as it was 40 years ago.