Why does switching from Mersenne twister to other

Question

I've been trying to create a generalized Gradient Noise generator (which doesn't use the hash method to get gradients). The code is below:

class GradientNoise {
    std::uint64_t m_seed;
    std::uniform_int_distribution<std::uint8_t> distribution;
    const std::array<glm::vec2, 4> vector_choice = {glm::vec2(1.0, 1.0), glm::vec2(-1.0, 1.0), glm::vec2(1.0, -1.0),
                                                    glm::vec2(-1.0, -1.0)};

public:
    GradientNoise(uint64_t seed) {
        m_seed = seed;
        distribution = std::uniform_int_distribution<std::uint8_t>(0, 3);
    }

    // 0 -> 1
    // just passes the value through, origionally was perlin noise activation
    double nonLinearActivationFunction(double value) {
        //return value * value * value * (value * (value * 6.0 - 15.0) + 10.0);
        return value;
    }

    // 0 -> 1
    //cosine interpolation
    double interpolate(double a, double b, double t) {
        double mu2 = (1 - cos(t * M_PI)) / 2;
        return (a * (1 - mu2) + b * mu2);
    }

    double noise(double x, double y) {
        std::mt19937_64 rng;
        //first get the bottom left corner associated
        // with these coordinates
        int corner_x = std::floor(x);
        int corner_y = std::floor(y);

        // then get the respective distance from that corner
        double dist_x = x - corner_x;
        double dist_y = y - corner_y;

        double corner_0_contrib; // bottom left
        double corner_1_contrib; // top left
        double corner_2_contrib; // top right
        double corner_3_contrib; // bottom right

        std::uint64_t s1 = ((std::uint64_t(corner_x) << 32) + std::uint64_t(corner_y) + m_seed);
        std::uint64_t s2 = ((std::uint64_t(corner_x) << 32) + std::uint64_t(corner_y + 1) + m_seed);
        std::uint64_t s3 = ((std::uint64_t(corner_x + 1) << 32) + std::uint64_t(corner_y + 1) + m_seed);
        std::uint64_t s4 = ((std::uint64_t(corner_x + 1) << 32) + std::uint64_t(corner_y) + m_seed);


        // each xy pair turns into distance vector from respective corner, corner zero is our starting corner (bottom
        // left)
        rng.seed(s1);
        corner_0_contrib = glm::dot(vector_choice[distribution(rng)], {dist_x, dist_y});

        rng.seed(s2);
        corner_1_contrib = glm::dot(vector_choice[distribution(rng)], {dist_x, dist_y - 1});


        rng.seed(s3);
        corner_2_contrib = glm::dot(vector_choice[distribution(rng)], {dist_x - 1, dist_y - 1});


        rng.seed(s4);
        corner_3_contrib = glm::dot(vector_choice[distribution(rng)], {dist_x - 1, dist_y});


        double u = nonLinearActivationFunction(dist_x);
        double v = nonLinearActivationFunction(dist_y);


        double x_bottom = interpolate(corner_0_contrib, corner_3_contrib, u);
        double x_top = interpolate(corner_1_contrib, corner_2_contrib, u);
        double total_xy = interpolate(x_bottom, x_top, v);
        return total_xy;
    }
};

I then generate an OpenGL texture to display with like this:

int width = 1024;
int height = 1024;
unsigned char *temp_texture = new unsigned char[width*height * 4];
double octaves[5] = {2,4,8,16,32};

for( int i = 0; i < height; i++){
    for(int j = 0; j < width; j++){
        double d_noise = 0;
        d_noise += temp_1.noise(j/octaves[0], i/octaves[0]);
        d_noise += temp_1.noise(j/octaves[1], i/octaves[1]);
        d_noise += temp_1.noise(j/octaves[2], i/octaves[2]);
        d_noise += temp_1.noise(j/octaves[3], i/octaves[3]);
        d_noise += temp_1.noise(j/octaves[4], i/octaves[4]);
        d_noise/=5;
        uint8_t noise = static_cast<uint8_t>(((d_noise * 128.0) + 128.0));
        temp_texture[j*4 + (i * width * 4) + 0] = (noise);
        temp_texture[j*4 + (i * width * 4) + 1] = (noise);
        temp_texture[j*4 + (i * width * 4) + 2] = (noise);
        temp_texture[j*4 + (i * width * 4) + 3] = (255);
    }
}

Which give good results:

But gprof is telling me that the Mersenne twister is taking up 62.4% of my time and growing with larger textures. Nothing else individual takes any where near as much time. While the Mersenne twister is fast after initialization, the fact that I initialize it every time I use it seems to make it pretty slow.

This initialization is 100% required for this to make sure that the same x and y generates the same gradient at each integer point (so you need either a hash function or seed the RNG each time).

I attempted to change the PRNG to both the linear congruential generator and Xorshiftplus, and while both ran orders of magnitude faster, they gave odd results:

LCG (one time, then running 5 times before using)

Xorshiftplus

After one iteration

After 10,000 iterations.

I've tried:

Running the generator several times before utilizing output, this results in slow execution or simply different artifacts.

Using the output of two consecutive runs after initial seed to seed the PRNG again and use the value after wards. No difference in result.

What is happening? What can i do to get faster results that are of the same quality as the mersenne twister?

OK BIG UPDATE:

I don't know why this works, I know it has something to do with the prime number utilized, but after messing around a bit, it appears that the following works:

Step 1, incorporate the x and y values as seeds separately (and incorporate some other offset value or additional seed value with them, this number should be a prime/non trivial factor)

Step 2, Use those two seed results into seeding the generator again back into the function (so like geza said, the seeds made were bad)

Step 3, when getting the result, instead of using modulo number of items (4) trying to get, or & 3, modulo the result by a prime number first then apply & 3. I'm not sure if the prime being a mersenne prime matters or not.

Here is the result with prime = 257 and xorshiftplus being used! (note I used 2048 by 2048 for this one, the others were 256 by 256)

Answer 1

What you see is the practical demonstration of quality of PRNG. Mersenne Twister is one of the best PRNGs with good performance, it passes DIEHARD tests. One should know that generating a random numbers is not an easy computational task, so looking for a better performance will inevitably result in poor quality. LCG is known to be simplest and worst PRNG ever designed and it clearly shows two-dimensional correlation as in your picture. The quality of Xorshift generators largely depend on bitness and parameters. They are definitely worse than Mersenne Twister, but some (xorshift128+) may work good enough to pass BigCrush battery of TestU01 tests.

In other words, if you are making an important physical modelling numerical experiment, you better continue to use Mersenne Twister as known to be a good trade-off between speed and quality and it comes in many standard libraries. On a less important case you may try to use xorshift128+ generator. For an ultimate results you need to use cryptographical-quality PRNG (none of mentioned here may be used for cryptographical purposes).

Answer 2

The randomness of LCGs are known to be sensitive to the choice of their parameters. In particular, the period of a LCG is relative to the m parameter - at most it will be m (your prime factor) & for many values it can be less.

Similarly, the careful parameters selection is required to get a long period from Xorshift PRNGs.

You've noted that some PRNGs give good procedural generation results while other do not. In order to isolate the cause, I would factor out the proc gen stuff & examine the PRNG output directly. An easy way to visualize the data is to build a grey scale image where each pixel value is a (possibly scaled) random value. For image based stuff, I find this to be an easy way to find stuff that may lead to visual artifacts. Any artifacts you see with this are likely to cause issues with your proc gen output.

Another option is to try something like the Diehard tests. If the aforementioned image test failed to reveal any problems, I might use this just to be sure my PRNG techniques were trustworthy.

Answer 3

LCG is known to be inadequate for your purpose.

Xorshift128+'s results are bad, because it needs good seeding. And providing good seeding defeats the whole purpose of using it. I don't recommend this.

However, I recommend using an integer hash. For example, one from Bob's page.

Here's a result of the first hash of that page, it looks OK to me, and it is fast (I think it is much faster than Mersenne Twister):

Here's the code I've written to generate this:

#include <cmath>
#include <stdio.h>

unsigned int hash(unsigned int a) {
    a = (a ^ 61) ^ (a >> 16);
    a = a + (a << 3);
    a = a ^ (a >> 4);
    a = a * 0x27d4eb2d;
    a = a ^ (a >> 15);
    return a;
}

unsigned int ivalue(int x, int y) {
    return hash(y<<16|x)&0xff;
}

float smooth(float x) {
    return 6*x*x*x*x*x - 15*x*x*x*x + 10*x*x*x;
}

float value(float x, float y) {
    int ix = floor(x);
    int iy = floor(y);
    float fx = smooth(x-ix);
    float fy = smooth(y-iy);

    int v00 = ivalue(iy+0, ix+0);
    int v01 = ivalue(iy+0, ix+1);
    int v10 = ivalue(iy+1, ix+0);
    int v11 = ivalue(iy+1, ix+1);
    float v0 = v00*(1-fx) + v01*fx;
    float v1 = v10*(1-fx) + v11*fx;
    return v0*(1-fy) + v1*fy;
}

unsigned char pic[1024*1024];

int main() {
    for (int y=0; y<1024; y++) {
        for (int x=0; x<1024; x++) {
            float v = 0;

            for (int o=0; o<=9; o++) {
                v += value(x/64.0f*(1<<o), y/64.0f*(1<<o))/(1<<o);
            }

            int r = rint(v*0.5f);

            pic[y*1024+x] = r;
        }
    }

    FILE *f = fopen("x.pnm", "wb");
    fprintf(f, "P5\n1024 1024\n255\n");
    fwrite(pic, 1, 1024*1024, f);
    fclose(f);
}

If you want to understand, how a hash function work (or better yet, which properties a good hash have), check out Bob's page, for example this.

Answer 4

You (unknowingly?) implemented a visualization of PRNG non-random patterns. That looks very cool!

Except Mersenne Twister, all your tested PRNGs do not seem fit for your purpose. As I have not done further tests myself, I can only suggest to try out and measure further PRNGs.

Answer 5

Note that your code seeds the PRNG, then generates one random number from the PRNG. The reason for the nonrandomness in xorshift128+ that you discovered is that xorshift128+ simply adds the two halves of the seed (and uses the result mod 2⁶⁴ as the generated number) before changing its state (review its source code). This makes that PRNG considerably different from a hash function.