ankerl::nanobench::Rng Class Reference

An extremely fast random generator. More...

#include <nanobench.h>

Public Types
using	result_type = uint64_t
	This RNG provides 64bit randomness. More...

Public Member Functions
	Rng (Rng const &)=delete
	As a safety precausion, we don't allow copying. More...
Rng &	operator= (Rng const &)=delete
	Same as Rng(Rng const&), we don't allow assignment. More...
	Rng (Rng &&) noexcept=default
Rng &	operator= (Rng &&) noexcept=default
	~Rng () noexcept=default
	Rng ()
	Creates a new Random generator with random seed. More...
	Rng (uint64_t seed) noexcept
	Rng (uint64_t x, uint64_t y) noexcept
	Rng (std::vector< uint64_t > const &data)
	ANKERL_NANOBENCH (NODISCARD) Rng copy() const noexcept
	Creates a copy of the Rng, thus the copy provides exactly the same random sequence as the original. More...
uint64_t	operator() () noexcept
	Produces a 64bit random value. More...
uint32_t	bounded (uint32_t range) noexcept
	Generates a random number between 0 and range (excluding range). More...
double	uniform01 () noexcept
	Provides a random uniform double value between 0 and 1. More...
template<typename Container >
void	shuffle (Container &container) noexcept
	Shuffles all entries in the given container. More...
std::vector< uint64_t >	state () const
	Extracts the full state of the generator, e.g. More...

Static Public Member Functions
static constexpr uint64_t()	min ()
static constexpr uint64_t()	max ()

Static Private Member Functions
static constexpr uint64_t	rotl (uint64_t x, unsigned k) noexcept

Private Attributes
uint64_t	mX
uint64_t	mY

Detailed Description

An extremely fast random generator.

Currently, this implements RomuDuoJr, developed by Mark Overton. Source: http://www.romu-random.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 drop-in 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 non-standard helpers, optimized for speed.

Definition at line 477 of file nanobench.h.

Member Typedef Documentation

result_type

using ankerl::nanobench::Rng::result_type = uint64_t

This RNG provides 64bit randomness.

Definition at line 482 of file nanobench.h.

Constructor & Destructor Documentation

Rng() [1/6]

ankerl::nanobench::Rng::Rng ( Rng const &  )

delete

As a safety precausion, 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().

Rng() [2/6]

ankerl::nanobench::Rng::Rng ( Rng &&  )

defaultnoexcept

~Rng()

ankerl::nanobench::Rng::~Rng ( )

defaultnoexcept

Rng() [3/6]

ankerl::nanobench::Rng::Rng

(

)

Creates a new Random generator with random seed.

Instead of a default seed (as the random generators from the STD), this properly seeds the random generator from std::random_device. It guarantees correct seeding. Note that seeding can be relatively slow, depending on the source of randomness used. So it is best to create a Rng once and use it for all your randomness purposes.

Rng() [4/6]

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.

Rng() [5/6]

ankerl::nanobench::Rng::Rng ( uint64_t  x, uint64_t  y  )

noexcept

Rng() [6/6]

ankerl::nanobench::Rng::Rng

(

std::vector< uint64_t > const &

data

)

Member Function Documentation

ANKERL_NANOBENCH()

ankerl::nanobench::Rng::ANKERL_NANOBENCH ( NODISCARD  ) const

noexcept

Creates a copy of the Rng, thus the copy provides exactly the same random sequence as the original.

bounded()

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 1129 of file nanobench.h.

max()

static constexpr uint64_t() ankerl::nanobench::Rng::max ( )

staticconstexpr

min()

static constexpr uint64_t() ankerl::nanobench::Rng::min ( )

staticconstexpr

operator()()

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 2-3 times as fast.

Returns

uint64_t The next 64 bit random value.

Definition at line 1119 of file nanobench.h.

operator=() [1/2]

Rng & ankerl::nanobench::Rng::operator= ( Rng &&  )

defaultnoexcept

operator=() [2/2]

Rng & ankerl::nanobench::Rng::operator= ( Rng const &  )

delete

Same as Rng(Rng const&), we don't allow assignment.

If you need a new Rng create one with the default constructor Rng().

rotl()

constexpr uint64_t ankerl::nanobench::Rng::rotl ( uint64_t  x, unsigned  k  )

staticconstexprprivatenoexcept

Definition at line 1155 of file nanobench.h.

shuffle()

template<typename Container >

void ankerl::nanobench::Rng::shuffle ( Container &  container )

noexcept

Shuffles all entries in the given container.

Although this has a slight bias due to the implementation of bounded(), this is preferable to std::shuffle because it is over 5 times faster. See Daniel Lemire's blog post Fast random shuffling.

Parameters

container

The whole container will be shuffled.

Definition at line 1145 of file nanobench.h.

state()

std::vector< uint64_t > ankerl::nanobench::Rng::state

(

)

const

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:

uniform01()

double ankerl::nanobench::Rng::uniform01 ( )

inlinenoexcept

Provides a random uniform double value between 0 and 1.

This uses the method described in Generating uniform doubles in the unit interval, and is extremely fast.

Returns

double Uniformly distributed double value in range [0,1(, excluding 1.

Definition at line 1135 of file nanobench.h.

Member Data Documentation

mX

uint64_t ankerl::nanobench::Rng::mX

private

Definition at line 597 of file nanobench.h.

mY

uint64_t ankerl::nanobench::Rng::mY

private

Definition at line 598 of file nanobench.h.

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The documentation for this class was generated from the following file:

• /work/src/bench/nanobench.h