What is performance-wise the best way to generate

Question

I need to generate random Boolean values on a performance-critical path.

The code which I wrote for this is

std::random_device   rd;
std::uniform_int_distribution<> randomizer(0, 1);
const int val randomizer(std::mt19937(rd()));
const bool isDirectionChanged = static_cast<bool>(val);

But do not think that this is the best way to do this as I do not like doing static_cast<bool>.

On the web I have found a few more solutions

1. std::bernoulli_distribution

2. bool randbool = rand() & 1; Remember to call srand() at the beginning.

Answer 1

If performance is your only criterion, then the answer is:

bool get_random()
{
    return true; // chosen by fair coin flip.
                 // guaranteed to be random.
}

Unfortunately, the entropy of this random number is zero, but the performance is quite fast.

Since I suspect that this random number generator is not very useful to you, you will need to quantify how random you want your booleans to be. How about a cycle length of 2048? One million? 2^19937-1? Until the end of the universe?

I suspect that, since you explicitly stated that performance is your utmost concern, then a good old fashioned linear congruential generator might be "good enough". Based on this article, I'm guessing that this generator's period is around 32*((2^31)-5), or about 68 trillion iterations. If that's not "good enough", you can drop in any C++11 compatible generator you like instead of minstd_rand.

For extra credit, and a small performance hit, modify the below code to use the biased coin algorithm to remove bias in the generator.

#include <iostream>
#include <random>

bool get_random()
{
    typedef std::minstd_rand generator_type;
    typedef generator_type::result_type result_type;

    static generator_type generator;
    static unsigned int bits_remaining = 0;
    static result_type random_bits;

    if ( bits_remaining == 0 )
    {
        random_bits = generator();
        bits_remaining = sizeof( result_type ) * CHAR_BIT - 1;
    }

    return ( ( random_bits & ( 1 << bits_remaining-- ) ) != 0 );
}

int main()
{
    for ( unsigned int i = 0; i < 1000; i++ )
    {
        std::cout << " Choice " << i << ": ";
        if ( get_random() )
            std::cout << "true";
        else
            std::cout << "false";

        std::cout << std::endl;
    }
}

Answer 2

Some quick benchmarks (code):

   647921509 RandomizerXorshiftPlus
   821202158 BoolGenerator2 (reusing the same buffer)
  1065582517 modified Randomizer
  1130958451 BoolGenerator2 (creating a new buffer as needed)
  1140139042 xorshift128plus
  2738780431 xorshift1024star
  4629217068 std::mt19937
  6613608092 rand()
  8606805191 std::bernoulli_distribution
 11454538279 BoolGenerator
 19288820587 std::uniform_int_distribution

For those who want ready-to-use code, I present XorShift128PlusBitShifterPseudoRandomBooleanGenerator, a tweaked version of RandomizerXorshiftPlus from the above link. On my machine, it is about as fast as @SergeRogatch's solution, but consistently about 10-20% faster when the loop count is high (≳100,000), and up to ~30% slower with smaller loop counts.

class XorShift128PlusBitShifterPseudoRandomBooleanGenerator {
public:
  bool randBool() {
    if (counter == 0) {
      counter = sizeof(GeneratorType::result_type) * CHAR_BIT;
      random_integer = generator();
    }
    return (random_integer >> --counter) & 1;
  }

private:
  class XorShift128Plus {
  public:
    using result_type = uint64_t;

    XorShift128Plus() {
      std::random_device rd;
      state[0] = rd();
      state[1] = rd();
    }

    result_type operator()() {
      auto x = state[0];
      auto y = state[1];
      state[0] = y;
      x ^= x << 23;
      state[1] = x ^ y ^ (x >> 17) ^ (y >> 26);
      return state[1] + y;
    }

  private:
    result_type state[2];
  };

  using GeneratorType = XorShift128Plus;

  GeneratorType generator;
  GeneratorType::result_type random_integer;
  int counter = 0;
};

Answer 3

Apparently I have to add another answer. Just figured out that starting with Ivy Bridge architecture Intel added RdRand CPU instruction and AMD added it later in June 2015. So if you are targeting a processor that is new enough and don't mind using (inline) assembly, the fastest way to generate random bools should be in calling RdRand CPU instruction to get a 64-bit random number as described here (scroll to approximately the middle of the page for code examples) (at that link there is also a code example for checking the current CPU for support of RdRand instruction, and see also the Wikipedia for an explanation of how to do this with CPUID instruction), and then use the bits of that number for booleans as described in my Xorshit+ based answer.

Answer 4

iI think that best way is an using of precalculated random array:

uint8_t g_rand[UINT16_MAX];
bool InitRand()
{
    for (size_t i = 0, n = UINT16_MAX; i < n; ++i)
        g_rand[i] = ::rand() & 1;
    return true;
}
bool g_inited = InitRand();
inline const uint8_t * Rand()
{
    return g_rand + (::rand()&INT16_MAX);
}

It using to fill some array dst[size]:

const size_t size = 10000;
bool dst[size];
for (size_t i = 0; i < size; i += INT16_MAX)
     memcpy(dst + i, Rand(), std::min<size_t>(INT16_MAX, size - col));

Of course you can initialize pre-calculated array with using of another random function.

Answer 5

if performance is important, perhaps it's a good idea to generate a 32 bit random number and use each separate bit of it, something like this:

bool getRandBool() {
    static uint32_t randomnumber;
    static int i=0;
    if (i==0) {
        randomnumber = <whatever your favorite randonnumbergenerator is>;
        i=32;
    }
    return (randomnumber & 1<<--i); 
 }

this way the generation only impacts every 32th call

Answer 6

For the purpose of performance, at a price of less "randomness" than e.g. std::mt19937_64, you can use Xorshift+ to generate 64-bit numbers and then use the bits of those numbers as pseudo-random booleans.

Quoting the Wikipedia:

This generator is one of the fastest generators passing BigCrush

Details: http://xorshift.di.unimi.it/ . There is a comparison table in the middle of the page, showing that mt19937_64 is 2 times slower and is systematic.

Below is sample code (the real code should wrap it in a class):

#include <cstdint>
#include <random>
using namespace std;

random_device rd;
/* The state must be seeded so that it is not everywhere zero. */
uint64_t s[2] = { (uint64_t(rd()) << 32) ^ (rd()),
    (uint64_t(rd()) << 32) ^ (rd()) };
uint64_t curRand;
uint8_t bit = 63;

uint64_t xorshift128plus(void) {
    uint64_t x = s[0];
    uint64_t const y = s[1];
    s[0] = y;
    x ^= x << 23; // a
    s[1] = x ^ y ^ (x >> 17) ^ (y >> 26); // b, c
    return s[1] + y;
}

bool randBool()
{
    if(bit >= 63)
    {
        curRand = xorshift128plus();
        bit = 0;
        return curRand & 1;
    }
    else
    {
        bit++;
        return curRand & (1<<bit);
    }
}