[Bench!][Mir] +54%..+185% performance boost for Mersenne Twister.

[Ilya Yaroshenko via Digitalmars-d]

Hey,

32-bit Mt19937 random number Generator is default in Phobos.
It is default in Mir too, except that 64-bit targets use 64-bit 
Mt19937 instead.

The last Mir Random beta improves performance for Mt19937.

The goal was to:

1. Improve RNG generation performance by making code more 
friendly for CPU pipelining. Tempering (finalization) operations 
was mixed with internal payload update operations.

2. Reduce number of required comparison operations using 
unification with add/sub operations. The order of payload 
iteration was reversed (the engine generates the same numbers as 
before).

3. Reduce memory access twice both for generation (required for 
1) and initialization. This will make the algorithm more 
cache-friendly for real world applications. Ditto (1)

Bench results:
----
mir.random 32-bit Mt19937:
     6.80851 Gb/s

mir.random 64-bit Mt19937:
    12.5984 Gb/s

std.random 32-bit Mt19937:
    4.41989 Gb/s

std.random 64-bit Mt19937:
     wrong initialization and tempering (finalization)
----
54%: 1.54 = 6.80851 / 4.41989
185%: 2.85 = 12.5984 / 4.41989

The benchmark can be found here:
https://github.com/libmir/mir-random/blob/master/bench/Mt19937_bench.d

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https://github.com/libmir :

  - mir - Generic numeric library
  - dcv - D Computer Vision library
  - mir-glas - Linear Algebra Subprograms (written in D)
  - mir-random - random numbers generators, includes non-uniform 
distributions.
  - mir-cpuid - CPU identification

Future:
  - mir-runtime - lightweight always inlined `nothrow @nogc` Dlang 
Runtime
  - mir-neural - neural network library [WIP]
  - mir-fft - Fast and multidimensional FFT
  - mir-svm - support vector machines

Cheers,
Ilya & Mir Team