I'm looking for a pseudo-random number generator that is "replayable" and "stateless". Let me elaborate: I need to be able to re-fetch a pseudo-random number based on a parameter to the random function. For example (C-style pseudocode):
int x1 = random(1);
int x2 = random(2);
// and so on with lots of random() calls in between
int new_x1 = random(1);
// now new_x1 is like a "replay" of x1, so x1 == new_x1
The type of arguments doesn't matter (I can typecast whatever is needed), the return value doesn't have to be int; ultimately I'll need 8-bit values.
The question is: what's a good PRNG algorithm that satisfies the requirement that the next pseudo-random value is controlled by a parameter, and not by its internal state which is updated upon each invocation? I don't what to use a crummy solution like the following:
int random(int input) {
srand(input);
return rand();
}
This would have to initialize the PRNG upon every invocation, which seems costly. (I am illustrating this point using the standard srand() / rand(), I know there are better algorithms out there, like Mersenne Twister, but the idea is still the same.)
One approach that might work here would be to use a block cipher like AES or triple-DES. Your pseudorandom generator could then be
int pseudorandomValue(int input) {
return encryptUsingAES(input);
}
This is stateless, pseudorandom (since the outputs of AES should be statistically indistinguishable from random), and stateless.
Hope this helps!
You may use the Xorshift[1,2] based PRNG. This PRNG uses the previous random number to generate the next. The implementation is very efficient, as compared to AES.
For 32-bit implementation:
uint32_t next_rand(uint32_t prev)
{
prev ^= prev << 13;
prev ^= prev >> 17;
prev ^= prev << 5;
return prev;
}
For 64-bit implementation:
uint64_t next_rand(uint64_t prev)
{
prev ^= prev << 21;
prev ^= prev >> 35;
prev ^= prev << 4;
return prev;
}
The random number sequence is "replayable", stateless, and depends on only the initial value, which is the seed.
References:
Wiki:Wiki.
A paper with detailed math: paper.
Related
So, I'm trying to create a random vector (think geometry, not an expandable array), and every time I call my random vector function I get the same x value, though y and z are different.
int main () {
srand ( (unsigned)time(NULL));
Vector<double> a;
a.randvec();
cout << a << endl;
return 0;
}
using the function
//random Vector
template <class T>
void Vector<T>::randvec()
{
const int min=-10, max=10;
int randx, randy, randz;
const int bucket_size = RAND_MAX/(max-min);
do randx = (rand()/bucket_size)+min;
while (randx <= min && randx >= max);
x = randx;
do randy = (rand()/bucket_size)+min;
while (randy <= min && randy >= max);
y = randy;
do randz = (rand()/bucket_size)+min;
while (randz <= min && randz >= max);
z = randz;
}
For some reason, randx will consistently return 8, whereas the other numbers seem to be following the (pseudo) randomness perfectly. However, if I put the call to define, say, randy before randx, randy will always return 8.
Why is my first random number always 8? Am I seeding incorrectly?
The issue is that the random number generator is being seeded with a values that are very close together - each run of the program only changes the return value of time() by a small amount - maybe 1 second, maybe even none! The rather poor standard random number generator then uses these similar seed values to generate apparently identical initial random numbers. Basically, you need a better initial seed generator than time() and a better random number generator than rand().
The actual looping algorithm used is I think lifted from Accelerated C++ and is intended to produce a better spread of numbers over the required range than say using the mod operator would. But it can't compensate for always being (effectively) given the same seed.
I don't see any problem with your srand(), and when I tried running extremely similar code, I did not repeatedly get the same number with the first rand(). However, I did notice another possible issue.
do randx = (rand()/bucket_size)+min;
while (randx <= min && randx >= max);
This line probably does not do what you intended. As long as min < max (and it always should be), it's impossible for randx to be both less than or equal to min and greater than or equal to max. Plus, you don't need to loop at all. Instead, you can get a value in between min and max using:
randx = rand() % (max - min) + min;
I had the same problem exactly. I fixed it by moving the srand() call so it was only called once in my program (previously I had been seeding it at the top of a function call).
Don't really understand the technicalities - but it was problem solved.
Also to mention, you can even get rid of that strange bucket_size variable and use the following method to generate numbers from a to b inclusively:
srand ((unsigned)time(NULL));
const int a = -1;
const int b = 1;
int x = rand() % ((b - a) + 1) + a;
int y = rand() % ((b - a) + 1) + a;
int z = rand() % ((b - a) + 1) + a;
A simple quickfix is to call rand a few times after seeding.
int main ()
{
srand ( (unsigned)time(NULL));
rand(); rand(); rand();
Vector<double> a;
a.randvec();
cout << a << endl;
return 0;
}
Just to explain better, the first call to rand() in four sequential runs of a test program gave the following output:
27592
27595
27598
27602
Notice how similar they are? For example, if you divide rand() by 100, you will get the same number 3 times in a row. Now take a look at the second result of rand() in four sequential runs:
11520
22268
248
10997
This looks much better, doesn't it? I really don't see any reason for the downvotes.
Your implementation, through integer division, ignores the smallest 4-5 bit of the random number. Since your RNG is seeded with the system time, the first value you get out of it will change only (on average) every 20 seconds.
This should work:
randx = (min) + (int) ((max - min) * rand() / (RAND_MAX + 1.0));
where
rand() / (RAND_MAX + 1.0)
is a random double value in [0, 1) and the rest is just shifting it around.
Not directly related to the code in this question, but I had same issue with using
srand ((unsigned)time(NULL)) and still having same sequence of values being returned from following calls to rand().
It turned out that srand needs to called on each thread you are using it on separately. I had a loading thread that was generating random content (that wasn't random cuz of the seed issue). I had just using srand in the main thread and not the loading thread. So added another srand ((unsigned)time(NULL)) to start of loading thread fixed this issue.
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)
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.
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.
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.
Note that your code seeds the PRNG, then generates one pseudorandom 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.
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).
What I need
I need an algorithm that produces a bijective output. I have a 31-bit input and need a pseudo-random 31-bit output.
What I have considered
CRCs are bijective within their bit-width.
I have looked on Google and can find the polynomials for this, but not the tables or algorithm.
Could anyone point me in the right direction?
I need a CRC-31 algorithm using polynomial say 0x737e312b, or any bijective function that will do what I need.
NOTE
I found the following code, but I unfortunately don't have the tools to compile and run it.
For any hash function hash, you can do:
function bijectiveHash31(int val) {
val &= 0x7FFFFFFF; //make sure it's 31 bits
for (int i=0; i<5; ++i) {
// the high bits affect the low bits
val ^= hash(val>>15) & 32767;
// rotate bits
val = ((val&32767)<<16) | ((val>>15)&65535);
}
return val;
}
This is a Feistel structure, which forms the basis of many ciphers: https://en.wikipedia.org/wiki/Feistel_cipher
If you need it to be fast and you don't need it to be super good, then this works fine:
function bijectiveHash31(int val) {
val = ((val*RANDOM_ODD_NUMBER) + RANDOM_NUMBER) & 0x7FFFFFFF;
val ^= (val>>15);
val ^= (val>>8);
return val;
}
In both of these cases, it's not too difficult to figure out how you could undo each elementary operation, which shows that the whole hash is bijective. If you need help establishing that for the multiplication, see https://en.wikipedia.org/wiki/Modular_multiplicative_inverse
I do not intend to use this for security purposes or statistical analysis. I need to create a simple random number generator for use in my computer graphics application. I don't want to use the term "random number generator", since people think in very strict terms about it, but I can't think of any other word to describe it.
it has to be fast.
it must be repeatable, given a particular seed.
Eg: If seed = x, then the series a,b,c,d,e,f..... should happen every time I use the seed x.
Most importantly, I need to be able to compute the nth term in the series in constant time.
It seems, that I cannot achieve this with rand_r or srand(), since these need are state dependent, and I may need to compute the nth in some unknown order.
I've looked at Linear Feedback Shift registers, but these are state dependent too.
So far I have this:
int rand = (n * prime1 + seed) % prime2
n = used to indicate the index of the term in the sequence. Eg: For
first term, n ==1
prime1 and prime2 are prime numbers where
prime1 > prime2
seed = some number which allows one to use the same function to
produce a different series depending on the seed, but the same series
for a given seed.
I can't tell how good or bad this is, since I haven't used it enough, but it would be great if people with more experience in this can point out the problems with this, or help me improve it..
EDIT - I don't care if it is predictable. I'm just trying to creating some randomness in my computer graphics.
Use a cryptographic block cipher in CTR mode. The Nth output is just encrypt(N). Not only does this give you the desired properties (O(1) computation of the Nth output); it also has strong non-predictability properties.
I stumbled on this a while back, looking for a solution for the same problem. Recently, I figured out how to do it in low-constant O(log(n)) time. While this doesn't quite match the O(1) requested by the author, It may be fast enough (a sample run, compiled with -O3, achieved performance of 1 billion arbitrary index random numbers, with n varying between 1 and 2^48, in 55.7s -- just shy of 18M numbers/s).
First, the theory behind the solution:
A common type of RNGs are Linear Congruential Generators, basically, they work as follows:
random(n) = (m*random(n-1) + b) mod p
Where m and b, and p are constants (see a reference on LCGs for how they are chosen). From this, we can devise the following using a bit of modular arithmetic:
random(0) = seed mod p
random(1) = m*seed + b mod p
random(2) = m^2*seed + m*b + b mod p
...
random(n) = m^n*seed + b*Sum_{i = 0 to n - 1} m^i mod p
= m^n*seed + b*(m^n - 1)/(m - 1) mod p
Computing the above can be a problem, since the numbers will quickly exceed numeric limits. The solution for the generic case is to compute m^n in modulo with p*(m - 1), however, if we take b = 0 (a sub-case of LCGs sometimes called Multiplicative congruential Generators), we have a much simpler solution, and can do our computations in modulo p only.
In the following, I use the constant parameters used by RANF (developed by CRAY), where p = 2^48 and g = 44485709377909. The fact that p is a power of 2 reduces the number of operations required (as expected):
#include <cassert>
#include <stdint.h>
#include <cstdlib>
class RANF{
// MCG constants and state data
static const uint64_t m = 44485709377909ULL;
static const uint64_t n = 0x0000010000000000ULL; // 2^48
static const uint64_t randMax = n - 1;
const uint64_t seed;
uint64_t state;
public:
// Constructors, which define the seed
RANF(uint64_t seed) : seed(seed), state(seed) {
assert(seed > 0 && "A seed of 0 breaks the LCG!");
}
// Gets the next random number in the sequence
inline uint64_t getNext(){
state *= m;
return state & randMax;
}
// Sets the MCG to a specific index
inline void setPosition(size_t index){
state = seed;
uint64_t mPower = m;
for (uint64_t b = 1; index; b <<= 1){
if (index & b){
state *= mPower;
index ^= b;
}
mPower *= mPower;
}
}
};
#include <cstdio>
void example(){
RANF R(1);
// Gets the number through random-access -- O(log(n))
R.setPosition(12345); // Goes to the nth random number
printf("fast nth number = %lu\n", R.getNext());
// Gets the number through standard, sequential access -- O(n)
R.setPosition(0);
for(size_t i = 0; i < 12345; i++) R.getNext();
printf("slow nth number = %lu\n", R.getNext());
}
While I presume the author has moved on by now, hopefully this will be of use to someone else.
If you're really concerned about runtime performance, the above can be made about 10x faster with lookup tables, at the cost of compilation time and binary size (it also is O(1) w.r.t the desired random index, as requested by OP)
In the version below, I used c++14 constexpr to generate the lookup tables at compile time, and got to 176M arbitrary index random numbers per second (doing this did however add about 12s of extra compilation time, and a 1.5MB increase in binary size -- the added time may be mitigated if partial recompilation is used).
class RANF{
// MCG constants and state data
static const uint64_t m = 44485709377909ULL;
static const uint64_t n = 0x0000010000000000ULL; // 2^48
static const uint64_t randMax = n - 1;
const uint64_t seed;
uint64_t state;
// Lookup table
struct lookup_t{
uint64_t v[3][65536];
constexpr lookup_t() : v() {
uint64_t mi = RANF::m;
for (size_t i = 0; i < 3; i++){
v[i][0] = 1;
uint64_t val = mi;
for (uint16_t j = 0x0001; j; j++){
v[i][j] = val;
val *= mi;
}
mi = val;
}
}
};
friend struct lookup_t;
public:
// Constructors, which define the seed
RANF(uint64_t seed) : seed(seed), state(seed) {
assert(seed > 0 && "A seed of 0 breaks the LCG!");
}
// Gets the next random number in the sequence
inline uint64_t getNext(){
state *= m;
return state & randMax;
}
// Sets the MCG to a specific index
// Note: idx.u16 indices need to be adapted for big-endian machines!
inline void setPosition(size_t index){
static constexpr auto lookup = lookup_t();
union { uint16_t u16[4]; uint64_t u64; } idx;
idx.u64 = index;
state = seed * lookup.v[0][idx.u16[0]] * lookup.v[1][idx.u16[1]] * lookup.v[2][idx.u16[2]];
}
};
Basically, what this does is splits the computation of, for example, m^0xAAAABBBBCCCC mod p, into (m^0xAAAA00000000 mod p)*(m^0xBBBB0000 mod p)*(m^CCCC mod p) mod p, and then precomputes tables for each of the values in the 0x0000 - 0xFFFF range that could fill AAAA, BBBB or CCCC.
RNG in a normal sense, have the sequence pattern like f(n) = S(f(n-1))
They also lost precision at some point (like % mod), due to computing convenience, therefore it is not possible to expand the sequence to a function like X(n) = f(n) = trivial function with n only.
This mean at best you have O(n) with that.
To target for O(1) you therefore need to abandon the idea of f(n) = S(f(n-1)), and designate a trivial formula directly so that the N'th number can be calculated directly without knowing (N-1)'th; this also render the seed meaningless.
So, you end up have a simple algebra function and not a sequence. For example:
int my_rand(int n) { return 42; } // Don't laugh!
int my_rand(int n) { 3*n*n + 2*n + 7; }
If you want to put more constraint to the generated pattern (like distribution), it become a complex maths problem.
However, for your original goal, if what you want is constant speed to get pseudo-random numbers, I suggest to pre-generate it with traditional RNG and access with lookup table.
EDIT: I noticed you have concern with a table size for a lot of numbers, however you may introduce some hybrid model, like a table of N entries, and do f(k) = g( tbl[k%n], k), which at least provide good distribution across N continue sequence.
This demonstrates an PRNG implemented as a hashed counter. This might appear to duplicate R.'s suggestion (using a block cipher in CTR mode as a stream cipher), but for this, I avoided using cryptographically secure primitives: for speed of execution and because security wasn't a desired feature.
If we were trying to create a secure stream cipher with your requirement that any emitted sequence be trivially repeatable, given knowledge of its index...
...then we could choose a secure hash algorithm (like SHA256) and a counter with a lot of bits (maybe 2048 -> sequence repeats every 2^2048 generated numbers without reseeding).
HOWEVER, the version I present here uses Bob Jenkins' famous hash function (simple and fast, but not secure) along with a 64-bit counter (which is as big as integers can get on my system, without needing custom incrementing code).
Code in main demonstrates that knowledge of the RNG's counter (seed) after initialization allows a PRNG sequence to be repeated, as long as we know how many values were generated leading up to the repetition point.
Actually, if you know the counter's value at any point in the output sequence, you will be able to retrieve all values generated previous to that point, AND all values which will be generated afterward. This only involves adding or subtracting ordinal differences to/from a reference counter value associated with a known point in the output sequence.
It should be pretty easy to adapt this class for use as a testing framework -- you could plug in other hash functions and change the counter's size to see what kind of impact there is on speed as well as the distribution of generated values (the only uniformity analysis I did was to look for patterns in the screenfuls of hexadecimal numbers printed by main()).
#include <iostream>
#include <iomanip>
#include <ctime>
using namespace std;
class CHashedCounterRng {
static unsigned JenkinsHash(const void *input, unsigned len) {
unsigned hash = 0;
for(unsigned i=0; i<len; ++i) {
hash += static_cast<const unsigned char*>(input)[i];
hash += hash << 10;
hash ^= hash >> 6;
}
hash += hash << 3;
hash ^= hash >> 11;
hash += hash << 15;
return hash;
}
unsigned long long m_counter;
void IncrementCounter() { ++m_counter; }
public:
unsigned long long GetSeed() const {
return m_counter;
}
void SetSeed(unsigned long long new_seed) {
m_counter = new_seed;
}
unsigned int operator ()() {
// the next random number is generated here
const auto r = JenkinsHash(&m_counter, sizeof(m_counter));
IncrementCounter();
return r;
}
// the default coontructor uses time()
// to seed the counter
CHashedCounterRng() : m_counter(time(0)) {}
// you can supply a predetermined seed here,
// or after construction with SetSeed(seed)
CHashedCounterRng(unsigned long long seed) : m_counter(seed) {}
};
int main() {
CHashedCounterRng rng;
// time()'s high bits change very slowly, so look at low digits
// if you want to verify that the seed is different between runs
const auto stored_counter = rng.GetSeed();
cout << "initial seed: " << stored_counter << endl;
for(int i=0; i<20; ++i) {
for(int j=0; j<8; ++j) {
const unsigned x = rng();
cout << setfill('0') << setw(8) << hex << x << ' ';
}
cout << endl;
}
cout << endl;
cout << "The last line again:" << endl;
rng.SetSeed(stored_counter + 19 * 8);
for(int j=0; j<8; ++j) {
const unsigned x = rng();
cout << setfill('0') << setw(8) << hex << x << ' ';
}
cout << endl << endl;
return 0;
}
I need some help to parallelize the pi calculation with the monte carlo method with openmp by a given random number generator, which is not thread safe.
First: This SO thread didn't help me.
My own try is the following #pragma omp statements. I thought the i, x and y vars should be init by each thread and should than be private. z ist the sum of all hits in the circle, so it should be summed after the implied barriere after the for loop.
Think the main problem ist the static state var of the random number generator. I made a critical section where the functions are called, so that only one thread per time could execute it. But the Pi solutions doesn't scale with more higher values.
Note: I should not use another RNG, but its okay to make little changes on it.
int main (int argc, char *argv[]) {
int i, z = 0, threads = 8, iters = 100000;
double x,y, pi;
#pragma omp parallel firstprivate(i,x,y) reduction(+:z) num_threads(threads)
for (i=0; i<iters; ++i) {
#pragma omp critical
{
x = rng_doub(1.0);
y = rng_doub(1.0);
}
if ((x*x+y*y) <= 1.0)
z++;
}
pi = ((double) z / (double) (iters*threads))*4.0;
printf("Pi: %lf\n", pi);;
return 0;
}
This RNG is actually an included file, but as I'm not sure if I create the header file correct, I integrated it in the other program file, so I have only one .c file.
#define RNG_MOD 741025
int rng_int(void) {
static int state = 0;
return (state = (1366 * state + 150889) % RNG_MOD);
}
double rng_doub(double range) {
return ((double) rng_int()) / (double) ((RNG_MOD - 1)/range);
}
I've also tried to make the static int state global, but it doesn't change my result, maybe I done it wrong. So please could you help me make the correct changes? Thank you very much!
Your original linear congruent PRNG has a cycle length of 49400, therefore you are only getting 29700 unique test points. This is a terrible generator to be used for any kind of Monte Carlo simulations. Even if you make 100000000 trials, you won't get any closer to the true value of Pi because you are simply repeating the same points over and over again and as a result both the final value of z and iters are simply multiplied by the same constant, which cancel in the end during the division.
The per-thread seed introduced by Z boson improves the situation a little bit with the number of unique points increasing with the total number of OpenMP threads. The increase is not linear since if the seed of one PRNG falls in the sequence of another PRNG, both PRNGs produce the same sequence shifted with no more than 49400 elements. Given the cycle length, each PRNG covers 49400/RNG_MOD = 6,7% of the total output range and that is the probability of two PRNGs being synchronised. There are a total of RNG_MOD/49400 = 15 unique sequences possible. It basically means that in the best seeding case scenario you won't be able to get past 30 threads as any other thread would simply repeat the result of some of the others. The multiplier 2 comes from the fact that each point uses two elements from the sequence and therefore it is possible to get a different set of points if you shift the sequence by one element.
The ultimate solution is to completely drop your PRNG and stick to something like Mersenne twister MT19937, which has a cycle length of 219937 − 1 and a very strong seeding algorithm. If you are not able to use another PRNG as you state in your question, at least modify the constants of the LCG to match those used in rand():
int rng_int(void) {
static int state = 1;
// & 0x7fffffff is equivalent to modulo with RNG_MOD = 2^31
return (state = (state * 1103515245 + 12345) & 0x7fffffff);
}
Note that rand() is not a good PRNG - it is still bad. It is just a little better than the one used in your code.
Try the code below. It makes a private state for each thread. I did something similar with the at rand_r function Why does calculation with OpenMP take 100x more time than with a single thread?
Edit: I updated my code using some of Hristo's suggestions. I used threadprivate (for the first time). I also used a better rand function which gives a better estimate of pi but it's still not good enough.
One strange things was I had to define the function rng_int after threadprivate otherwise I got an error "error: 'state' declared 'threadprivate' after first use". I should probably ask a question about this.
//gcc -O3 -Wall -pedantic -fopenmp main.c
#include <omp.h>
#include <stdio.h>
#define RNG_MOD 0x80000000
int state;
int rng_int(void);
double rng_doub(double range);
int main() {
int i, numIn, n;
double x, y, pi;
n = 1<<30;
numIn = 0;
#pragma omp threadprivate(state)
#pragma omp parallel private(x, y) reduction(+:numIn)
{
state = 25234 + 17 * omp_get_thread_num();
#pragma omp for
for (i = 0; i <= n; i++) {
x = (double)rng_doub(1.0);
y = (double)rng_doub(1.0);
if (x*x + y*y <= 1) numIn++;
}
}
pi = 4.*numIn / n;
printf("asdf pi %f\n", pi);
return 0;
}
int rng_int(void) {
// & 0x7fffffff is equivalent to modulo with RNG_MOD = 2^31
return (state = (state * 1103515245 + 12345) & 0x7fffffff);
}
double rng_doub(double range) {
return ((double)rng_int()) / (((double)RNG_MOD)/range);
}
You can see the results (and edit and run the code) at http://coliru.stacked-crooked.com/a/23c1753a1b7d1b0d