An extremely fast random generator.
Currently, this implements RomuDuoJr, developed by Mark Overton. Source: http://www.romurandom.org/
RomuDuoJr is extremely fast and provides reasonable good randomness. Not enough for large jobs, but definitely good enough for a benchmarking framework.
 Estimated capacity: bytes
 Register pressure: 4
 State size: 128 bits
This random generator is a dropin replacement for the generators supplied by <random>
. It is not cryptographically secure. It's intended purpose is to be very fast so that benchmarks that make use of randomness are not distorted too much by the random generator.
Rng also provides a few nonstandard helpers, optimized for speed.
Definition at line 488 of file nanobench.h.
ankerl::nanobench::Rng::Rng 
( 
Rng const & 
 ) 


delete 
As a safety precaution, we don't allow copying.
Copying a PRNG would mean you would have two random generators that produce the same sequence, which is generally not what one wants. Instead create a new rng with the default constructor Rng(), which is automatically seeded from std::random_device
. If you really need a copy, use copy()
.
ankerl::nanobench::Rng::Rng 
( 
uint64_t 
seed  ) 


explicitnoexcept 
Creates a new Rng that is seeded with a specific seed. Each Rng created from the same seed will produce the same randomness sequence. This can be useful for deterministic behavior.
embed:rst
.. note::
The random algorithm might change between nanobench releases. Whenever a faster and/or better random
generator becomes available, I will switch the implementation.
As per the Romu paper, this seeds the Rng with splitMix64 algorithm and performs 10 initial rounds for further mixing up of the internal state.
 Parameters

seed  The 64bit seed. All values are allowed, even 0. 
uint32_t ankerl::nanobench::Rng::bounded 
( 
uint32_t 
range  ) 


inlinenoexcept 
Generates a random number between 0 and range (excluding range).
The algorithm only produces 32bit numbers, and is slightly biased. The effect is quite small unless your range is close to the maximum value of an integer. It is possible to correct the bias with rejection sampling (see here, but this is most likely irrelevant in practices for the purposes of this Rng.
See Daniel Lemire's blog post A fast alternative to the modulo reduction
 Parameters

range  Upper exclusive range. E.g a value of 3 will generate random numbers 0, 1, 2. 
 Returns
 uint32_t Generated random values in range [0, range(.
Definition at line 1175 of file nanobench.h.
uint64_t ankerl::nanobench::Rng::operator() 
( 
 ) 


inlinenoexcept 
Produces a 64bit random value.
This should be very fast, thus it is marked as inline. In my benchmark, this is ~46 times faster than std::default_random_engine
for producing 64bit random values. It seems that the fastest std contender is std::mt19937_64
. Still, this RNG is 23 times as fast.
 Returns
 uint64_t The next 64 bit random value.
Definition at line 1165 of file nanobench.h.
constexpr uint64_t ankerl::nanobench::Rng::rotl 
( 
uint64_t 
x, 


unsigned 
k 

) 
 

staticconstexprprivatenoexcept 
Extracts the full state of the generator, e.g.
for serialization. For this RNG this is just 2 values, but to stay API compatible with future implementations that potentially use more state, we use a vector.
 Returns
 Vector containing the full state:
Definition at line 1206 of file nanobench.h.