~~NOTRANS~~
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====== Testing, Reproducibility and Random Numbers ======

===== General =====

Software needs testing .. and many tests can be automized with unittests - or integration tests.\\
The program/test should be reproducible to allow (later) debugging/fixing.

===== Crash early! =====

Especially when the program or it's results must be reliable, **crashing early** might be advised!\\
In this case, additional checks should be enforced and executed. Note, that the C ''assert()'' is removed in non-debug compiled binaries.

Besides executing explicitly programmed checks, there are also other possibilities:
  * setup signal handler, e.g. segmentation fault (SIGSEGV)
  * setup floating point traps, see [[development:numeric_math|Numeric / Math / Linear Algebra]]
  * compile with automatic checks
    * see https://gcc.gnu.org/onlinedocs/libstdc++/manual/using_macros.html for
      * ''_GLIBCXX_DEBUG''
      * ''_GLIBCXX_ASSERTIONS''
      * ''_GLIBCXX_SANITIZE_VECTOR''
    * see https://gcc.gnu.org/onlinedocs/gcc/Instrumentation-Options.html for
      * sanitizers like the prominent AddressSanitizer 'ASan'
      * stack-protector
      * and others

A welcomed side effect of crashing early:\\
the stack trace and the visible variable contents might reveal the initial cause of the problem\\
- at least with a much higher probability than after having continued and covered the problem.


===== Core-Dumps =====

A core-dump / minidump mechanism might be of interest.\\
For tracking very rare or specific errors, this mechanism might also get delivered to customers.


===== Pseudo Random Number Generators (PRNG) =====

For tests, a huge amount of '//random//' numbers can be generated, from one //seed//-number.\\
There's no need to have a cryptographically safe PRNG.\\
Test with defined //seed//-numbers or simply log/save the used seed number.

==== Algorithms / Libraries ====

=== Properties ===

  * size of the //seed// or //state//
    * small size is of interest to use one PRNG per thread
  * quick initialization from seed
  * speed, for producing next number
  * period size - until the sequence repeats

=== Linear congruential generator (LCG) ===

//LCG// is perhaps the simplest (and fastest) algorithm with a small seed; see https://en.wikipedia.org/wiki/Linear_congruential_generator

//Fast skipping// might be of interest; see https://www.nayuki.io/page/fast-skipping-in-a-linear-congruential-generator

=== Xorshift ===

Also a very fast PRNG. Here some links

  * https://en.wikipedia.org/wiki/Xorshift
  * [[https://prng.di.unimi.it/|xoshiro / xoroshiro generators]]
  * http://pracrand.sourceforge.net/
  * https://sourceforge.net/projects/gjrand/
  * http://simul.iro.umontreal.ca/testu01/tu01.html

=== Mersenne Twister ===

This PRNG got hyped in the C++ community for it's ridiculously huge period size - but has a big seed size and it's initialization isn't the fastest. IMHO, in most applications, the huge period size isn't necessary.

Anyway, here a link to a SIMD-oriented implementation: http://www.math.sci.hiroshima-u.ac.jp/m-mat/MT/SFMT/index.html

=== Other Links ===

  * PCG - comparison of Mersenne Twister, Arc4, ..
    * https://www.pcg-random.org/
  * 64 bit version of Mersenne Twister
    * http://www.math.sci.hiroshima-u.ac.jp/m-mat/MT/emt64.html
      * https://www.reddit.com/r/programming/comments/2momvr/pcg_a_family_of_better_random_number_generators/
      * https://github.com/imneme/pcg-cpp
  * https://en.cppreference.com/w/cpp/numeric/random/uniform_int_distribution
  * CppCon 2016: Cheinan Marks “I Just Wanted a Random Integer!
    * https://www.youtube.com/watch?v=4_QO1nm7uJs
  * CppCon 2016: Walter E. Brown "What C++ Programmers Need to Know about Header <random>"
    * https://www.youtube.com/watch?v=6DPkyvkMkk8
  * CppCon 2022: Roth Michaels "Fast, High-Quality Pseudo-Random Numbers for Non-Cryptographers"
    * https://www.youtube.com/watch?v=I5UY3yb0128
    * https://github.com/CppCon/CppCon2022/blob/main/Presentations/Fast-High-Quality-Pseudo-Random-Numbers-CPPCon2022-Roth-Michaels.pdf
  * Vectorized (SIMD) generation
    * https://lemire.me/blog/2018/07/23/are-vectorized-random-number-generators-actually-useful/
    * https://forum.juce.com/t/fast-simd-based-random-number-generator-whether-and-how-to-vectorize-random-nextint/44430
  * Github
    * https://github.com/imneme/pcg-cpp
    * https://github.com/DEShawResearch/random123
    * https://github.com/Reputeless/Xoshiro-cpp