A Little Bit About PRNG Stuff

Let’s talk about Pseudorandom Number Generators, which are called PRNGs for short.

In general, a PRNG is a deterministic way to take some state and perform a step so that you’ll have both a new state and also some random-seeming output value.

There’s lots of possible PRNGs. There’s even families of PRNGs, where they all use the same basic math and just a few constants are different between each particular generator (different multipliers, different offsets, different state size, etc).

Today we’re not going to invent a totally new PRNG, we’re going to implement an existing one.

Also, in this article we’ll need to talk about “X to the power of Y” type values. In math, if you can’t write actual superscript text, sometimes you’d write X^Y, but in Rust the ^ character means bitwise_xor. Since Rust doesn’t naturally have any operator for the power function we’ll go ahead and steal ** from Python. So if we want “X to the power of Y” we’ll write X**Y.

Generator Criteria

There’s several categories that we might care about when considering a PRNG.

First of all we can consider the size of the generator’s state. This is very easy to quantify: how many bytes do we need for our struct? In general, we’d like all our structs to be as small as possible, and this is no different.

We also want the speed of the step function to be as fast as possible. This is also pretty easy to quantify, we can set up benchmarks and check the speed of different step functions for different generators. If we care about bulk randomness generation we might measure the output in terms of GB/second, which makes for a better comparison between generators that have different amounts of output per step.

The period of the generator is how many times you can step the generator before the output sequence loops around. Since they’re deterministic, eventually all PRNGs will loop around. The period of a generator is at most equal to the number of possible bit states within the generator. If there’s only 8 bits of state, that’s only 256 possible states, so you can’t have more than a period of 256. However, not all generator state is always used with maximum efficiency towards having a long period, so sometimes the period is less than the number of possible bit states. Even if you don’t plan to actually loop the generator, you usually want different runs of the program (or different threads within a single program) to not re-use parts of the sequence that you’ve gone over before, so a bigger period is always nicer to have. On modern machines, 2**64 should be the minimum acceptable generator period.

When we talk about the quality of the output generator we mean how random-seeming each output is. Is the output biased? Can the output be predicted based on the previous outputs? Stuff like that. Checking these things involves running the generator to get output and then doing statistical tests on that output. You’re not expected to know how to do that yourself. Instead, there’s test suites that are available that will run your generator and then do the statistics on the output and print out some results. TestU01 is a C library that has the SmallCrush, Crush, and BigCrush test batteries. PractRand can be used as a library or as a CLI tool.

A generator has uniform output if every possible output value appears an equal number of times in the full output sequence. It could be exactly once for each possible output, or it could be more than once, as long as all outputs appear an equal number of times. This might seem very simple, but some generators don’t do this. If a generator is known to be uniform and you go through more than about half of the period, the apparent quality of the output begins to slowly drop, until the final output value becomes perfectly guessable. Since all outputs showed up X many times, except one particular output which showed up X-1 times, the final output has to be the one that was X-1 times. In practice this isn’t a problem for realistic sized generators, because no one would precisely log all of the outputs of your generator anyway.

A concept that’s similar to uniformity, but not quite the same, is k-dimensional equidistribution. This means “how many times in a row can you get a generator output before the next output can’t be any possible value in the output range?”. If you’re using multiple generator calls aggregated together then you probably really care about this one. Say you’re generating 2D points, with one call for X and one for Y. You probably want to know if the point (1,1) can’t ever actually occur because the output sequence doesn’t ever have an output of 1 twice in a row. Maybe that chance of seeing a (1,1) is very small, but it should be possible right?

My Kingdom For A Hardware Multiplier

The types of PRNGs we’ll be focusing on today assume that your device is at least advanced enough to have efficient hardware integer multiplication. This might seem like a low bar, but lots of fun retro devices don’t have that, or they do but it’s costly. If you’re programming for something where multiplication is a heavy cost and you want something cheaper than that maybe check out the XorShift family of generators instead.

Because of the details of how the XorShift generator family works they never actually output 0, which means that they all have non-uniform output. I’d rather use generators that do have uniform output, so we won’t be looking at the XorShift stuff today.

The Generators We Will Talk About Here Are Not Cryptographically Secure

When a pseudorandom number generator meets some high enough standards to make it suitable for cryptography we call it a Cryptographically-secure pseudorandom number generator, or CSPRNG. This means that you can use the generator for keeping your secrets.

The generators that we’ll talk about here today are not cryptographically secure.

In other words, do not use these generators for cryptography.

I’m serious.

Linear Congruential Generators

One of the easiest types of generator to understand and implement is the Linear Congruential Generator (LCG). They’re so simple to implement that they’re used in countless programs. They’re even used in pokemon!

The LCG family of generators was developed in 1958 by W. E. Thomson and A. Rotenberg, based on the slightly earlier Lehmer Generator which had been developed by D. H. Lehmer in 1951. While there are some problems with this type of generator, they’re well understood problems that we can compensate for.

LCG Basics

The core formula of the LCG family is very simple. If you look at the wikipedia page they give it using the full fancy math notion, but the pseudocode version is:

// pseudocode

x_next = (a * x_current + c) `mod` m

The mod thing is something you might not be familiar with called Modular Arithmetic. Essentially the value of a * x_current + c is “wrapped” to be within 0 to m.

There’s some rules for all the parameters involved if we want it to work right:

Hmm, so we could just use % to do a modulus division, but that’s way expensive compared to the multiply and the add. Maybe we can like… get rid of the mod, or something? Math is usually flexible, you can rewrite expressions and stuff, it’s all fine as long as you follow the correct rewrite rules. Do we got any rewrite rules that can help us? Let’s check that wikipedia page. If we go to the “Integers modulo n” section there’s some stuff. There’s even some rewrite rules we can try for addition, subtraction, or multiplication.

// with OP as any of: addition, subtraction, or multiplication

(a OP b) `mod` m == ((a `mod` m) OP (b `mod` m)) `mod` m

Huh, well we’ve only got multiplication and addition in our LCG, and we’ve got rewrite rules for both of those operations. Let’s try isolating all of our equation’s components so that they’re on their own.

// starting formula
x_next = (a * x_current + c) `mod` m

// break up the addition
x_next = (((a * x_current) `mod` m) + (c `mod` m)) `mod` m

// break up the multiplication
x_next = ((((a `mod` m) * (x_current `mod` m)) `mod` m) + (c `mod` m)) `mod` m

That’s a lot of “mod m”. Are we closer to something useful? All of our terms and all of our operations are “mod m” all the time. Know what else is “mod m” all of the time? If you said “unsigned intergers” you get a point! We can use unsigned integers and then have everything happen “mod m” in the hardware automatically for us.

If we use 32-bit integers then m=2**32, if we use 64-bit integers then m=2**64, and so on. Which means that we can pick an unsigned integer type and cancel out all the “mod m” parts, which simplifies our formula back down to:

// still kinda pseudocode

x_next = a.wrapping_mul(x_current).wrapping_add(c)

Convenient! I told you an LCG would be pretty easy to implement after all.

Okay, let’s get some actual rust code going. We’ll use u64 values for everything, since that’s the biggest we can efficiently go on a 64-bit machine. We’ll also use const generics for the multiply and add value, since those values aren’t supposed to change while the generator is running. We’re gonna give them the name MUL and ADD though, so it’s more clear to others what they’re doing. And the value that changes from step to step can be, uh, the position? When you step around you’re changing your position, that makes sense. We don’t want to just call it “state” since in a little bit we’ll have some other state fields too.

#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
pub struct GenericLcg64<const MUL: u64, const ADD: u64> {
    position: u64,

impl<const MUL: u64, const ADD: u64> GenericLcg64<MUL, ADD> {
    pub fn next_u64(&mut self) -> u64 {
        self.position = self.position.wrapping_mul(MUL).wrapping_add(ADD);

Is our generator any good? Who knows! We can’t even turn it on right now because we don’t know what to put in for MUL and ADD.

Selecting Our LCG Parameters

Let’s go back to that Wikipedia page about how LCGs work. It says if we want the maximum period possible for our generator, and given that we’ve already selected m=2**64, then we need to have ADD != 0, and we have to make sure that we satisfy the following rules (the “Hull–Dobell Theorem”):

That’s still a lot of possible numbers. When in doubt, try to keep reading until it makes sense.

Although the Hull–Dobell theorem provides maximum period, it is not sufficient to guarantee a good generator.

Flip. Okay, well, there’s a chart of common parameters…

This table is to show popularity, not examples to emulate; many of these parameters are poor.

Ferris, please save us.

Tables of good parameters are available.[2][10]


Okay so let’s check out those references numbered 2 and 10:

Ah, hmm, well… okay we check the most recent paper first. We want those latest and greatest numbers. Here’s the direct link to the PDF if you don’t want to bother looking thought the arXiv website and finding the link yourself.

Steele / Vigna 2020

When reading a paper for the first time, always take a moment to double check the Abstract and the Introduction to make sure that what you think it’s talking about and what it thinks its talking about are the same thing.

In this paper, we provide lists of multipliers for both MCGs and LCGs, continuing the line of work by L’Ecuyer in his classic paper.

Paydirt! Oh hey there’s that github link again… maybe it lists some numbers? The ReadMe for the repo says their database of numbers is 14GB compressed, and 36GB decompressed. Sounds like the wrong avenue for us.

Ah, finally, Chapter 8: Tables.

There’s a wonderfully long explanation here of how all the different multipliers are ranked with spectral scores and harmonic scores and all sorts of things. The short version seems to be that, if we don’t really know what we’re doing:

So for an LCG with u64, we look in the chart and find 0xF691B575. There’s other values in the list we could also use, but we’ll use that one for now. We finally have a MUL value!

The ADD value is much simpler. We can pick any odd value, so we just need to make sure the lowest bit is always set. We could do that with ADD|1 of course. There’s a small problem though: when people pick a value “at random” they often enough pick a very small value where they’ll notice that, say, 2 and 3 are the same stream, since 2|1 and 3|1 are the same. Instead, we’ll take a hint from how the PCG does it and use (ADD<<1)|1. This throws out the highest bit of user input instead of the lowest, which they’re probably less likely to notice. We’ll look more into the PCG stuff later on, this isn’t the last trick we’re gonna borrow from them.

Let’s just update that step function…

impl<const MUL: u64, const ADD: u64> GenericLcg64<MUL, ADD> {
    pub fn next_u64(&mut self) -> u64 {
        self.position = self.position.wrapping_mul(MUL).wrapping_add((ADD<<1)|1);

Now we can also make an alias with “simple” parameters people can go by if they don’t want to figure out their own.

pub type SimpleLcg64 = GenericLcg64<0xF691B575, 0>;

Reducing The Output Size

Okay, so we’ve got a generator, but it’s not very good right now.

The problem that we’ve got is that every bit in an LCG’s state is on a 2**bit cycle, counting from 1 as the lowest bit. This means that our highest bit is on a 2**64 cycle, which is plenty large, but the lowest bit is on a 2**1 cycle. It’s just constantly flipping between 0 and 1.

Since the generator’s entire state is output at each step, this means that the predictable bits in the state become predictable bits in our output. Those top bits are fine, we just have an exponential fall off in quality as we go down the line of bits.

Well, maybe we don’t need more than say 32 bits from a single step of the generator. Right? That’s “big enough” nearly all the time. Let’s just adjust the generator to only return u32 values, and we can shift the upper half of the bits down and return those. Then we’d only be returning the better bits.

impl<const MUL: u64, const ADD: u64> GenericLcg64_32<MUL, ADD> {
    pub fn next_u32(&mut self) -> u32 {
        let out = (self.position >> 32) as u32;
        self.position = self.position.wrapping_mul(MUL).wrapping_add((ADD<<1)|1);

Note that our output is formed from the position value before it changes, not after. This lets us get just a tiny hair of instruction level parallelism going. This way computing the output doesn’t depend on the change computation having completed. When the step function is this small it can help to shave off even one cycle.

Now our generator has a new name! Before our generator was an “LCG64” type, because of a 64-bit position. Now that we’re just keeping the best 32 bits for the ourput it’s known as an “LCG64/32”. Of course there’s plenty of possible multipliers and additives, so there isn’t just exactly one LCG64/32. Like I said at the start, it’s a whole family of potential generators.

Testing With SmallCrush

Our generator isn’t the best. An LCG64/32 just fundamentally can’t pass any of the more intense randomness test suites. However, our generator should be able to at least pass the SmallCrush test suite from the TestU01 collection. Let’s check our work so far.

Thankfully, there’s already a blog post on how to test with TestU01, so we’ll mostly go by that. One minor problem: TestU01 is a C library. I don’t want to bother with making it callable from Rust and all that, so for our quick test we’ll just adapt the sample C program in the blog post into one that runs our LCG64/32. I’m not really a C programmer, but I know enough to do this little bit.

// adapted from https://www.pcg-random.org/posts/how-to-test-with-testu01.html

#include "TestU01.h"
#include <stdint.h>

static uint64_t y = 2456;

uint32_t lcg64() {
  uint32_t out = (uint32_t)(y>>32);
  y = y * 0xF691B575 + 1;
  return out;

int main() {
  unif01_Gen *gen = unif01_CreateExternGenBits("lcg64 blog", lcg64);
  return 0;

Okay, now we build the program from within our /TestU01 directory that we have from following the blog post instructions on how to get the library set up:

gcc -std=c99 -Wall -O3 -o lcg64 lcg64.c -Iinclude -Llib -ltestu01 -lprobdist -lmylib -lm

And finally we run the thing.

A whole ton of stuff spits out, and then at the end we see

========= Summary results of SmallCrush =========

 Version:          TestU01 1.2.3
 Generator:        lcg64 blog
 Number of statistics:  15
 Total CPU time:   00:00:07.65

 All tests were passed


Since an LCG64/32 can’t pass the stronger test batteries in TestU01 we’ll skip those for now. Passing SmallCrush is a good enough self check at this point. We’ll switch over to talking about some fun additional things we can let our LCG do.

Jumping The Generator Position

Here’s the first fun special technique that we can do with our LCG. If we want to send the generator forward by delta steps, without actually calling the generator’s step function delta times, we can! It’s described in a paper called “Random Number Generation with Arbitrary Stride”. If we look up that paper name we can get ourselves to the OSTI page for the paper, which… I guess we click find in google scholar? Right and… okay here’s a direct PDF Link, finally.

Let’s have a look. Hmm, the paper is showing the same formula as before, but they’re calling the multiplier g instead of a. Other than the different name it’s the same equation. Always helps to know you’re on the same page.

Next we’ve got a formula to directly jump a generator k steps forward:

s_(k) = (s_(0) * g**k + c * (g**k - 1)/(g-1)) `mod` 2**m

However, there’s a mild snag. More like a big snag. This formula assumes actual integers. You know, real integers like math uses, that don’t have bit limits. No wrapping values to stay within a specific bit width. To not accidentally run out of bits when using this formula, we’d need to do all the math at twice the bit width as our LCG position. With a u64 position, we could maybe do u128 math. If we wanted to scale things up and have a u128 position, well Rust doesn’t even have a u256 yet. There’s gonna be some better way, right?

Yeah, if we just turn to the next page we see some lovely follow-up formulas:

G = g**k `mod` 2**m
  = some sort of summation `mod` 2**m
  = some sort of product `mod` 2**m
  // I don't know how to write those ops out well,
  // if you wanna know the details look in the paper

C = c * (g**k - 1)/(g-1)) `mod` 2**m
  = c * (1 + g + g**2 + ... + g**(k-1)) `mod` 2**m

Does this help? Well the paper proposes that we rewrite things like so:

// before

s_(k) = (s_(0) * g**k + c * (g**k - 1)/(g-1)) `mod` 2**m

// after

G = ...
C = ...
s_(k) = (s_(0) * G + C) `mod` 2**m

So we’ll compute a value for G, which we’ll lovingly call “big G”, and also a value for C, “big C”. Both big G and big C will be normal u64 values. Then the final operation becomes a multiply and an add like we’ve already been doing.

We even get some pseudocode on precisely how to compute big G and big C while staying in the limits of common programming languages:

Algorithm G:
  G <- 1
  h <- g
  i <- (k + 2**m) `mod` 2**m
  while i > 0:
    if i is_odd:
      G <- (G * h) `mod` 2**m
    h <- (h * h) `mod` 2**m
    i <- i `intDiv` 2
  return G

Algorithm C:
  C <- 0
  f <- c
  h <- g
  i <- (k + 2**m) `mod` 2**m
  while i > 0:
    if i is_odd:
      C <- (C * h + f) `mod` 2**m
    f <- (f * (h+1)) `mod` 2**m
    h <- (h * h) `mod` 2**m
    i <- i `intDiv` 2
  return C

I’m sure you’re wondering, “what’s up with that i <- (k + 2**m) mod 2**m? Isn’t that a no-op?” The answer is that it’s sorta trying to say that this works if you want to “go negative steps”. That is, if you want to go to a previous state of the generator. Makes sense, since if you go far enough forward the generator output loops around eventually.

If you look at all that closely you’ll see that Algorithm G and Algorithm C share many common elements. We can merge the two loops as we convert it to Rust. The code is something like this:

impl<const MUL: u64, const ADD: u64> GenericLcg64_32<MUL, ADD> {
    pub fn jump_position(&mut self, delta: u64) {
        let mut big_g = 1_u64;
        let mut h = MUL;
        let mut big_c = 0_u64;
        let mut f = (ADD<<1)|1;
        let mut i = delta;
        while i > 0 {
            if (i & 1) != 0 {
                big_g = big_g.wrapping_mul(h);
                big_c = big_c.wrapping_mul(h).wrapping_add(f);
            f = f.wrapping_mul(h.wrapping_add(1));
            h = h.wrapping_mul(h);
            i /= 2;
        self.position = self.position.wrapping_mul(big_g).wrapping_add(big_c);

That’s a lot of stuff, we could have gotten something wrong pretty easily. Let’s add a test case too just to see if things work like we think.

fn test_lcg_u64_jump() {
    let g = StandardLcg64_32 { position: 5678 };

    let mut g_jumped1 = g.clone();
    let mut g_next1 = g.clone();
    assert_eq!(g_jumped1.position, g_next1.position);

    let mut next_back_one = g_next1.clone();
    next_back_one.jump_position(-1_i64 as u64);
    assert_eq!(g.position, next_back_one.position);

    let mut g_jumped50 = g.clone();
    let mut g_next50 = g.clone();
    for _ in 0..50 {
    assert_eq!(g_jumped50.position, g_next50.position);


Multiple Streams

When you change the additive value of your LCG it changes the ordering of all the outputs. If we wanted to have more than one generator we might want each one to have its own output order as well as its own position. The particular output ordering of a particular generator is the “stream” of the generator.

We can already have different output streams with different const generic values, but that also makes generators from separate output streams totally separate types. If we wanted our generators to still count as the same type of value, we can move the ADD const generic to be a field of our struct. This also lets us select the stream at runtime instead of only being able to pick at compile time.

pub struct MultiStream_GenericLcg64_32<const MUL: u64> {
    position: u64,
    stream: u64,

When you construct the generator you’d assign both a position and a stream, but when you step the generator only the position will change.

impl<const MUL: u64> MultiStream_GenericLcg64_32<MUL> {
    pub const fn new(position: u64, stream: u64) -> Self {
        Self {
          stream: (stream<<1) | 1,
    pub fn next_u32(&mut self) -> u32 {
        let out = (self.position >> 32) as u32;
        self.position = self.position.wrapping_mul(MUL).wrapping_add(self.stream);

We ensure that self.stream is odd in the new method using the same pattern as before. After we set it up it never needs to change again.

Also, if we had a jump function it would use self.stream instead of (ADD<<1)|1.

Moving a const generic into a struct field is a fairly obvious transformation, so I don’t think we need to write out every part of it. Still, you might want to consider this if you’re looking for a little more runtime variety without too much additional code complexity.

The downside of this generator alteration is that since the position is changing, but our stream doesn’t change, we’ve doubled our required state without increasing our generator’s period. At least it’s a very simple change, so people can understand it without a big explanation.

Digit Counter Generator

This is the first of two ways we can get k-dimensional equidistribution on our generator. Both of these are described by M.E. O’Neill in the PCG paper. One of them is described as being an LCG technique, and one is listed in the PCG section. Really, both of the extension techniques can be used by either type of generator.

This technique we’ll call a “Digit Counter” generator. To get our k-dimensional generator, we’ll have k inner generators all in operation. When the “lower digit” generators roll over they’ll “carry” and cause the next generator up to step an extra time.

Imagine we have a counter clicker device. It’s got a button, you push the button and the 1s digit goes up. When that rolls over from 9 to 0 then it carries and the 10s digit goes up. The pattern repeats up through all the digits, just that the highest digit doesn’t carry into anything else. Eventually you’ll see every possible digit combination, if you click through the entire sequence. So we know that if we have k different digit slots, then the overall sequence is k-dimensionally equidistributed when you read the counter and then use one digit at a time from whatever value you read.

A problem is that when we look at each successive block of output from the clicker, most of the digits are the same. Most of the time only a single digit is changing: 000, 001, 002, etc. That’s not very random seeming, is it? What we’ll do instead is advance every digit with every click, in addition the carry when a digit rolls over.

In the case of our Digit Counter Generator, each “digit” will be an entire LCG, and “advancing” the digit will be stepping the LCG once.

Chaining a bunch of identical LCGs together has a problem. Sometimes the overall collection of generators will end up closely in phase. Imagine when our clicker counter gets to 000. The next steps are 111, 222, etc. Eventually the 999 rolls to 110, but that’s 9 blocks of output in a row when all the digits are the same value. Not very great. It should be possible for all outputs to be the same value, but we don’t want all those moments right in a row. To prevent this we need each LCG to be a unique output ordering. This is where the “Multiple Streams” idea comes in. Each digit will be using its own stream. Some of the time you’ll still see all the digits have identical output, but they won’t have all those moments directly in a row.

Exactly how we do multiple streams per digit can be done in at least two ways:

As I’ve mentioned already, this particular generator works best for block output. If you’re outputting just one value at a time you need to store which “digit” you advanced last. Storing an index of “which digit is next” takes up state space which isn’t actually helping the generator in any of the criteria categories we care about. If we output an entire block of values at once we never store which digit is next, since every call touches every digit.

Let’s finally get to some code. For this generator we’ll have a method that takes a reference to an array and fills it in with new values. That saves us from a lot of accidental stack copying if next_block doesn’t get inlined. Also, for this exact example we’ll go with the “one const generic ADD value that we adjust automatically between the digits” style.

// note: can't derive Default, we'd have to write the impl ourselves.
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub struct DigitCounter_GenericLcg64_32<const MUL: u64, const ADD: u64, const DIGITS: usize> {
    digits: [u64; DIGITS],

impl<const MUL: u64, const ADD: u64, const DIGITS: usize>
    DigitCounter_GenericLcg64_32<MUL, ADD, DIGITS>
    /// Fills the array reference given with the next block of outputs.
    pub fn next_block(&mut self, target: &mut [u32; DIGITS]) {
        // gather output based on the *previous* step's results like we normally do.
            .for_each(|(t, d)| {
                *t = (d >> 32) as u32;
        // we just adjust a single add value as we go.
        let mut add = (ADD << 1) | 1;
        // the lowest digits always updates exactly once
        self.digits[0] = self.digits[0].wrapping_mul(MUL).wrapping_add(add);
        for i in 1..DIGITS {
            add += 2;
            self.digits[i] = self.digits[i].wrapping_mul(MUL).wrapping_add(add);
            // higher digits do an extra add if the previous digit landed on 0.
            if self.digits[i - 1] == 0 {
                self.digits[i] = self.digits[i].wrapping_add(add);

In addition to having the k-dimensional equidistribution that we were after, this generator will have a very large period. With a b-bit LCG per digit (period of 2**b), our combined k-digit generator has a period of 2**(b*k) blocks produced.

Unfortunately we do lose something by using this technique: the normal formula for jumping an LCG’s position doesn’t work with this combined digit generator. Each individual digit is a normal LCG, but the position jump formula doesn’t tell us if a jump passed over 0 or not, so we don’t know when we should advane the next higher digit by the extra amount.

Xor Array Generator

Having a Digit Counter Generator with block output is fine for some of our k-dimensional needs. On the other hand, we might want a generator that has normal “one value at a time” output, and just happens to also provide k-dimensional equidistribution as well if we use it that way. Like I said, the PCG paper has a second extension scheme we can use for this situation.

This time our generator will be fundamentally “one” generator, with only one position value. However, we will add an “extension array” which holds elements of our generator’s output type. The size of the extension array should be a power of 2, you’ll see why in a moment.

/// For unbiased results, `K` should be a power of 2.
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub struct ExtensionArray_GenericLcg64_32<const MUL: u64, const ADD: u64, const K: usize> {
    position: u64,
    ext: [u32; K],

Now every time we would output a value from the generator, instead we’re going to xor the value with an element from the extension array. We’ll select an extension array index at random by using bits from our position. If we want the index picked to be very predictable we can use the lowest bits which are very regular. Alternately we can use the highest bits and get a less predictable index. For our code, we’ll use % to turn the position value into a value from 0 to K-1. This is why we want K to be a power of two. If K is a power of two, our % operation (very slow) will be optimized into a bitand operation instead (very fast). Also, the index we get will be selected in a uniform way. If K is not a power of two we’ll have to do an actual modulus division, and the index will not be uniform.

So far with doing this xor step thing it’s kinda like we’ve mixed K different output sequences together. When we xor all the values in an output sequence with some other value, it changes the apparent ordering of the sequence. If we’ve got K differnet values to xor with, we’re mixing K differnet sequences together. By itself that’s not enough for k-dimensional equidistribution.

The final thing we need is a way to “advance” the extension array at some regular interval. We must “advance” the extension array at least once per position period. It’s also possible to advance the array more often, as long as the interval is regular. This “advance” operation to be something that causes all the array state bits to go through a full period.

Since we already talked about the digit generator, we can use a similar system for advancing the array. This time we can just use “+1” as the “per digit” advancement system for simplicity.

impl<const MUL: u64, const ADD: u64, const K: usize> ExtensionArray_GenericLcg64_32<MUL, ADD, K> {
    pub fn next_u32(&mut self) -> u32 {
        // mask with an extension array slot
        let out = (self.position >> 32) as u32 ^ self.ext[self.position as usize % K];
        // if our position is 0 we advance the array
        if self.position == 0 {
            let mut last_carried = false;
            self.ext.iter_mut().for_each(|x_mut| {
                let (new, carried) = x_mut.overflowing_add(1 + (last_carried as u32));
                *x_mut = new;
                last_carried = carried;
        // advance the position
        self.position = self.position.wrapping_mul(MUL).wrapping_add((ADD << 1) | 1);

The disadvantage of using “+1 per slot, with carry” as the extension array advancement scheme is that each extension array value doesn’t change much per array step. However, it does let us jump forward and back by X many base periods by just adding X or subtracting X (with carry) from all array slots. Having a way to cheaply jump around is kinda neat. If you don’t care about being able to cheaply jump, and in easy multiples, you can use an alternate update scheme. For example, we could add 1431655765 instead. Alternately written as 0x55555555 or 0b01010101_01010101_01010101_01010101, this constant will cause many more bits to change compared to adding just 1 (at least on average). Or maybe you don’t even add a value at all. You could run some other alteration on the bits. Like I said, any way of updating the array is fine as long as the array update scheme lets the array have a full period cycle.

The Xor Array generator also has a very large period. With a b-bit LCG base generator, r-bit xor array elements, and k array slots, we get a total period of 2**(b+(r*k)). This is 100% of our state bits, just like the Digit Counter Generator had. However, the Xor Array generator only produes a single element per generator step, not a block of k elements per step. This makes the Xor Array worse at bulk generation, if that’s your primary goal. On the other hand, if you want a generator that’s “mostly normal”, but can offer k-dimensional output when needed, the Xor Array generator is a good compromise.

Permuted Congruential Generators

We’ve seen many things we can do with an LCG, and I’ve mentioned a thing called a “PCG” too. The initials PCG stand for Permuted Congruential Generator, and you can think of it as “an LCG but better”.

The problem that we have with the LCG is that the bit quality drops drastically the lower we go down the line of bits. Our basic answer to the problem was “only keep the top half of the bits”. The PCG family uses the idea that if we apply a “permutation” to the raw LCG output we can get a much better output quality boost than a plain shift gives.


If you go and read the full PCG paper you’ll see that there isn’t just one permutation function provided. Instead, there’s several suggested permutation building blocks that you can mix together.

All the building blocks have fancy initials for short because you’re expected to combine more than one of them together and be able to name that combination.

The combined permutation has a big jumble of letters for a name that says what building blocks are used. Here are some examples:

// half size output

pub const fn xsh_rr_u64_u32(x: u64) -> u32 {
    let temp: u64 = x ^ (x >> 18);
    ((temp >> 27) as u32).rotate_right((x >> 59) as u32)

pub const fn xsh_rs_u64_u32(x: u64) -> u32 {
    let temp: u64 = x ^ (x >> 22);
    (temp >> (29 - (x >> 61))) as u32

pub const fn xsl_rr_u128_u64(x: u128) -> u64 {
    ((x as u64) ^ ((x >> 64) as u64)).rotate_right((x >> 122) as u32)

// full size output

pub const fn rxs_m_xs_u32(x: u32) -> u32 {
    let mut temp: u32 = x ^ (x >> (4 + (x >> 28)));
    temp = temp.wrapping_mul(277803737);
    temp ^ (temp >> 22)

pub const fn rxs_m_xs_u64(x: u64) -> u64 {
    let mut temp: u64 = x ^ (x >> (5 + (x >> 59)));
    temp = temp.wrapping_mul(12605985483714917081);
    temp ^ (temp >> 43)

pub const fn xsl_rr_rr_u128(x: u128) -> u128 {
    let low64 = ((x as u64) ^ ((x >> 64) as u64)).rotate_right((x >> 122) as u32);
    let high64 = ((x >> 64) as u64).rotate_right(low64 as u32 & 63);
    ((high64 as u128) << 64) | (low64 as u128)

Building A Pcg

Actually putting one of these PCG things together is about 98% the same as how an LCG works.

The only difference is that instead of doing (self.position >> 32) as u32 to get our output, we call whichever permutation function we want to use.

The general outline is like this:

#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
pub struct GenericPcg32_PERMUTATION_FN<const MUL: u64, const ADD: u64> {
    position: u64,
impl<const MUL: u64, const ADD: u64> GenericPcg32_PERMUTATION_FN<MUL, ADD> {
    /// Produces the next 32-bits of pseudorandom output.
    pub fn next_u32(&mut self) -> u32 {
        let out = PERMUTATION_FUNCTION(self.position);
        self.position = self.position.wrapping_mul(MUL).wrapping_add((ADD << 1) | 1);

Because the PCG is just applying an alternate transformation to the output, all of those tricks we saw before about jumping the position and extending the generator and all that can still be used here.

One note on the naming:

Generating Bounded Integers

Alright we’ve looked at the PCG paper, and we saw on pcg-random.org that there’s a post about how to setup TestU01. Are there any other blog posts that can help us? Ah, Efficiently Generating a Number in a Range sounds great. I love to efficiently verb things.

The basic idea of bounded in integers is that we have a function signature like this:

// example for u32, but would be the same for any unsigned int type.
// `range` should be 1 or more
pub const fn gen_in_range(mut rng: impl FnMut() -> u32, range: u32) -> u32;

Then gen_in_range will use the generator to create a value in in the range 0 .. range. Exactly how that happens depends on the method. Many example methods are given in the blog post.

I don’t want to just repeat what the blog post says, so I’ll assume that you’ve read it. Go do that right now if you haven’t.

Right, so it looks like we’ve got two strong contenders for getting numbers in a range in an unbiased way.

One of these is very simple and clearly correct. The other one is totally wild, and how the hell does that work at all?

I think we’ll look at the simple one first.

Bitmask with Rejection

Okay, for the bitmask system:

  1. Get a mask of all the bits that might be used in a valid output.
  2. Call the generator and apply the mask to its output.
  3. If the number is in range then return it, otherwise do step 2 again.
// note: for this example we're using `FnMut() -> u32` as a PRNG stand in.

pub fn bitmask_reject_u32(mut f: impl FnMut() -> u32, range: NonZeroU32) -> u32 {
    let mask = u32::MAX >> ((range.get() - 1) | 1).leading_zeros();
    loop {
        let out = f() & mask;
        if out < range.get() {
            return out;

I bet you’re wondering about that funky mask calculation. What’s happening is that we want our mask to be as tight as possible. If the mask allows in bits that are never going to be set in a valid output that just increases output rejections (which wastes time). We’re going to count the leading_zeroes to see what the highest bit that’s on is. However, there’s one case where a direct call to leading_zeros doesn’t quite give the right answer. If the range value is exactly a power of two it will have a leading_zeroes value that’s one less than the leading_zeroes of the highest possible output. That means that with a power of two range we’d let in one bit more than necessary with our mask. Since ideally all bits are on 50% of the time, that means that we’d trigger an unnecessary output rejection 50% of the time that we had an otherwise acceptable output. If we instead subtract 1 and then bitor 1 we get exactly the leading_zeros value we need.

 R:     N                 ((N-1)|1)
 1: 0b00000001 (clz 31), 0b00000001 (clz 31)
 2: 0b00000010 (clz 30), 0b00000001 (clz 31) <--
 3: 0b00000011 (clz 30), 0b00000011 (clz 30)
 4: 0b00000100 (clz 29), 0b00000011 (clz 30) <--
 5: 0b00000101 (clz 29), 0b00000101 (clz 29)
 6: 0b00000110 (clz 29), 0b00000101 (clz 29)
 7: 0b00000111 (clz 29), 0b00000111 (clz 29)
 8: 0b00001000 (clz 28), 0b00000111 (clz 29) <--
 9: 0b00001001 (clz 28), 0b00001001 (clz 28)
10: 0b00001010 (clz 28), 0b00001001 (clz 28)
11: 0b00001011 (clz 28), 0b00001011 (clz 28)
12: 0b00001100 (clz 28), 0b00001011 (clz 28)
13: 0b00001101 (clz 28), 0b00001101 (clz 28)
14: 0b00001110 (clz 28), 0b00001101 (clz 28)
15: 0b00001111 (clz 28), 0b00001111 (clz 28)
16: 0b00010000 (clz 27), 0b00001111 (clz 28) <--

That was the whole explanation. It’s not always the fastest but it’s plenty fast and very easy to understand.

Debiased Integer Multiplication

The second scheme that we’re told is going to be quite fast is “Debiased Integer Multiplication”, created by Lemire. When we convert the PCG Blog’s version into Rust we get something like this:

pub fn bounded_rand(mut rng: impl FnMut() -> u64, range: u32) -> u32 {
  let threshold = range.wrapping_neg() % range;
  loop {
    let x = rng.next_u32();
    let m = (x as u64) * (range as u64);
    let low_bits = m as u32;
    if low_bits >= threshold {
      return (m >> 32) as u32;

And uh, honestly, what? Like, really, for real, what? Why does this work? Don’t get me wrong it does work and you’ll get correct results, but what is happening here?

Alright let’s google the name of this method, and then there’s some hits, and one of them is a blog post in Lemire’s own blog. Let’s look at Nearly Divisionless Random Integer Generation On Various Systems. It even referrs to the PCG Blog post that we’re using right now. Oh, it says… that we can re-arrange the code a bit to try and avoid that % operation most of the time. Interesting, always nice to avoid a division and/or modulus operation. More importantly at the moment though, one of the links in the blog is to Fast Random Integer Generation in an Interval, a paper by Lemire. Let’s read that PDF and see what’s going on.

Only 13 pages, not bad. The Introduction so we’re talking about integers in the interval [0, s), which in Rust we’d write 0..s. That’s inclusive on the low bound (including 0) and exclusive on the upper bound (excluding s). Then we see some examples of when we might need these bounded numbers, like for shuffling an array. Next in Mathematical Notation we get some notes on how we’re supposed to decode the math runes in the paper. A ceiling op, a floor op, we’ve got the grade school division sign ÷ meaing integer division (standard division and then floor). Nothing too weird.

We do get a thing called Lemma 2.1:

The next section is called Existing Unbiased Techniques Found In Common Software Libraries, which just really sets into stone that Lemire doesn’t care for writing terse titles. Here it talks about how OpenBSD and Java do it.

Avoiding Division is where we finally get an explanation of what’s going on. The first thing we need to be aware of is that when we do an X-bit multiplication the “full” result is actually a (2*X)-bit result. If you multiply two 32-bit values, the full result is a 64-bit value. If you multiply two 64-bit values, the full result is a 128-bit value. He talks a bit about how non-Rust languages would go about getting the “high” bits from the operation. In Rust we have u128 as a standard type, so for both 32-bit and 64-bit multiplications we can just cast the values into the next bigger type and do a normal multiply. Okay, here’s the good bits, pay close attention. I’ll put them all with bullet points because this paragraph is pretty dense.

This lets us write Lemma 4.1:

That’s still pretty dense. Let’s walk through an example as we go over it.

We’ll do it with 8-bit values since that’s easier to understand. So that means that L is 8, and we’ll say that s is going to be 20.

The claim: for every x (a random u8 value), if we do a u16 multiply of x times s (20), then divide that down by 2**L (256), we’ll get a value in the range 0..s (0..20).

For each possible u8 value that x might be, when full multiplied by s (20), we get an intermediate u16 value m. Here’s a chart of what we might get:

x m=x*20
0 0
1 20
2 40
255 5100

What if s was any bigger? It could easily be bigger than 20, what if it’s so big we overflow a u16 and cause trouble? That’s not a concern with the way we’re doing it: the random number and the random number range have to be the same type. In other words, with random u8 values, the biggest possible s value is 255. It’s easy to check that 255 * 255 = 65025, which fits into a u16. More generally if you multiply the maximum value of any X-bit number it will always still fix into a 2X-bit value. This means that s can be any size that fits into the type without worry.

After the multiply, there’s a division by 2**L (256). This maps the m values back into the final range 0..s. Let’s look at another mini chart:

x m=x*20 out=m/256
0 0 0
1 20 0
12 240 0
13 260 1
14 280 1
25 500 1
26 520 2
27 540 2
255 5100 19

But not all of the outputs will appear an equal number of times (playground):

out # of times
0 13
1 13
2 13
3 13
4 12
5 13
6 13
7 13
8 13
9 12
10 13
11 13
12 13
13 13
14 12
15 13
16 13
17 13
18 13
19 12

Thus we have to apply the rejection step to some of the values. By Lemma 4.1: if (x * s) mod 2**L >= 2**L mod s, then we keep that x value.

Let’s have a second look at that threshold value in the Rust code above:

  let threshold = range.wrapping_neg() % range;

This is one of those “two ways to get to the same value” situations. A “bit hack” you might call it. Normally, to compute 2**L mod s, you’d need to have a variable that holds 2**L and then modulus divide by s. Except that 2**L is going to be outside our type’s range. For u8, then 2**8 would be 256, one more than the type’s maximum allowed value. This holds for all the other unsigned integers as well. Instead of having to use the next bigger unsigned integer type to do the modulus division (which ends up being even slower), we have this alternate expression. It gets us to the same value in the end. How exactly this expression was arrived at… I honestly don’t know. It’s just one of those congruence relation things that works when you’re doing modulus math.

If we use this rejection step then all outputs will occur exactly 12 times (playground).

Oh, but then Lemire wanted us to do some sort of “nearly divisionless” tactic. Let’s convert this suggested new version to Rust. It’s using u64 instead of u32, but otherwise we’ll try to make it as close as possible to our first version so that we can see the differences easily:

pub fn nearly_divisionless(mut rng: impl FnMut() -> u64, range: u64) -> u64 {
    let mut x: u64 = rng();
    let mut m: u128 = (x as u128) * (range as u128);
    let mut low_bits: u64 = m as u64;
    if low_bits < range {
        let threshold: u64 = range.wrapping_neg() % range;
        while low_bits < threshold {
            x = rng();
            m = (x as u128) * (range as u128);
            low_bits = m as u64;
    (m >> 64) as u64

We’ve moved the computation of the threshold value behind a check that low_bits is less than the range. This means that we can skip computing threshold at all in some cases, which skips a division. The assumption here is that range will always be greater than or equal to the threshold, so if low_bits isn’t less than range it also can’t be less than threshold, and we should skip computing the exact threshold at all. We can quickly check this for all possible u8, u16, and u32 values easily in the playground. There’s too many u64 values to keep going, but since it holds for all the smaller unsigned in types we can be fairly confident that this is true for all the unsigned int types.

Which To Use?

Going by the benchmarks in the PCG Blog, the debiased multiplication is a little faster better than bitmasking. This nearly divisionless version should be generally a little faster than the version benchmarked.

However, debiased multiplication requires a full width multiplication, so for u128 ranges we’d need to use the bitmasking technique.

Generating Normalized Floats

Say we want to have random floating point values, f32 or f64. Generating any floating point value is easy, f32::from_bits(rng.next_u32()) and it’s done. Same works for f64, you’d just call f64::from_bits with the output of next_u64 instead.

I can already hear the objections, “Lokathor, that’s practically garbage data! How do I put that into the range I want?” Of course, of course, I’m just briefly showing what’s possible.

Let’s back up a moment. We could get any integer value, but in practice we want a bounded range of integer values. The same holds for floats. We could get any f32 value, but in practice we want a bounded range of f32 values.

What Range Is A Good Range?

What type of random float ranges should we try to support?

Here we run into a problem: the distribution of float values isn’t actually even. There’s always more possible float values as you move toward zero than as you move away from it. Your math teacher might have once said something really abstract sounding like “there’s an infinite number of integers”, and this is true. Our computer’s int types only encode a handful of all possible integers. Might be a big handful, but it’s still limited. The same holds for floats. There’s an infinite number of real numbers, and our computer’s floating types encode only a handful of all possible real numbers.

With integer types, there’s a lower and upper value on the range, and all integers within the range are reprisented within the type. If we consider u8, the lower bound is 0, the upper bound is 255, but all possible integers in between are also allowed. This isn’t true with floating point types.

Let’s compare the ranges of f32 and i32:

Value f32 i32
Minimum -3.40282347e+38 -2147483648
Maximum 3.40282347e+38 2147483647

Each type has the same number of bits, so they have the same number of possible bit patterns. However, f32 has a much bigger range than i32 when we look at the minimum and maximum values. Also, f32 allows for fractional values like 2.5, which i32 does not. And there’s also “not-a-number” bit patterns in f32 (NaN for short). And there’s also Infinity and Negative Infinity. And there’s even a negative 0 value. Given all this, clearly some of the integer values that i32 can represent exactly cannot be reprisented exactly in f32. Since we also know there’s infinitely many fractional numbers, then it must be the case that some of those can’t be reprisented exactly using f32. Even if we use f64 instead of f32, the whole problem still happens, just with bigger values.

Which brings us back to the question we started with: what sort of random range do we want? With integer ranges, we only actually supported 0 to N. If you wanted a non-zero starting point we said you should use 0 to N and then apply an offset. For floats we want to pick as small a range as we can. This makes our proportion of actually representable values within the range be as high as possible. However, if the range is too small it’s harder to actually use.

Most people expect to be able to get “normalized” or “unit” float values, in the 0.0 to 1.0 range. We can also trivially support having a random sign bit, so -1.0 to 1.0 is possible as well. If you want any other kind range you can use multiplication and addition to adjust the unit value.

Binary Float Representation Review

In a previous draft I went way into the details of how binary floats are reprisented. It ended up being a little overkill, and not helpful to our goal of PRNG stuff. Instead, I’ll just throw out the list of the links that I read when reviewing the subject myself.

Generating Values In Our Range

The simple way to generate an f32 in 0.0 to 1.0 is usually written as something like:

let f = (rng.next_u32() as f32) / (u32::MAX as f32);

The idea of this technique is that since the numerator is 0 to u32::MAX, and the denominator is u32::MAX, then the output will be 0.0 to 1.0. And you do get values in that range. However, the distribution of the values is all garbage. This is because of that binary reprisentation of floats stuff that I just told you to review.

A binary float is a sign bit, some exponent bits, and some fraction bits. It all gets multiplied together to form a number (assuming that it’s a real number bit pattern). Since the exponent bits are base 2, that means that every power of two has as many values between 0.0 and that value as are above that value. Plenty of big integers can’t be exactly represented by a float of equivalent bits at all.

// u32::MAX is 4294967295
// but this just prints "4294967300" twice
fn main() {
    println!("{}", u32::MAX as f32);
    println!("{}", (u32::MAX-1) as f32);

So even though values will be in range with a division like this, the distribution will be completely horrible. Plenty of values in 0.0 to 1.0 won’t have a chance of being in the output when you use the simple division method.

This brings us around to a short paper called Generating Pseudo-random Floating-Point Values. Our good friend orlp on Github pointed me to this one. It looks at this exact problem and then (this is the part I like the most) it describes an easy to implement solution. To understand what it does you need to have at least a basic understanding of binary floats. This is your last chance if you haven’t read that other stuff yet.

The technique described in the paper works in three stages.

  1. First we need a random exponent. For this part we don’t want our usual uniform distribution of just grabbing some number of bits directly from the generator’s output. Instead we’ll follow a binomial distribution. Starting from an exponent of -1, we’ll check one bit at a time from our randomness. Each time a bit is 0 we subtract one from our exponent and loop again. When we see a bit that’s 1, or when we hit the minimum exponent, we stop. Alternately, we could look for 1s and stop on a 0, and we’d get the same distribution. It can be faster to look for 0s on x86 and x86_64, so we’ll go with
  2. Second we need a random mantissa. For this part we do use a uniform distribution. We simply call the generator, and then mask down to the correct number of bits.
  3. Third, if the mantissa value is 0, then we add one back on to our exponent 50% of the time. This final step is what lets us (very rarely) get an exponent of 0.

For our version, I think we’ll make the mantissa value first. Since the mantissa can affect the exponent generation, but doesn’t depend on anything itself, it just makes the most sense to me to do that part first. Also, it will help us avoid calling the generator too many times. Not only for speed benefits, which are nice, but also because we don’t know if our generator will be multi-dimensional equidistributed (most aren’t).

Another thing, the paper is concerned with making values of [0.0, 1.0] (inclusive at both ends), but if we also use a random bit to determine the sign bit of our output then we can produce values in the range [-1.0, 1.0] without changing anything else. We’ll do this with const generics so that people who are after the [0.0, 1.0] range don’t see any performance loss. When the function is monomorphized the optimizer will cut the dead code and redundant ops that deal with sign bit stuff.

pub fn gen_unit_f32<const SIGNED: bool>(mut rng: impl FnMut() -> u32) -> f32 {
    const MANTISSA_BIT_COUNT: u32 = f32::MANTISSA_DIGITS - 1;
    const MANTISSA_MASK: u32 = (1_u32 << MANTISSA_BIT_COUNT) - 1;
    const EXPONENT_SHIFT: u32 = 32 - 1 - MANTISSA_BIT_COUNT;
    const SIGN_SHIFT: u32 = 31;

    let mut bits: u32 = rng();
    let mut bit_count = 32;

    // generate mantissa, but keep spare bits stored to reduce generator calls
    debug_assert!(bit_count >= MANTISSA_BIT_COUNT);
    let mantissa: u32 = bits & MANTISSA_MASK;
    bits >>= MANTISSA_BIT_COUNT;
    bit_count -= MANTISSA_BIT_COUNT;

    // determine our initial exponent value
    let mut exponent: u32 = (1.0_f32.to_bits() >> MANTISSA_BIT_COUNT) - 1;
    debug_assert!(bit_count >= 1);
    if mantissa == 0 {
        if (bits & 1) != 0 {
            exponent += 1;
        bits >>= 1;
        bit_count >>= 1;

    // consume bits until we see a 1,
    // then decrease our exponent by the number of 0s we saw.
    'x: loop {
        if bits != 0 {
            let bits_consumed = bits.trailing_zeros() + 1;
            debug_assert!(bit_count >= bit_count);
            bits >>= bits_consumed;
            bit_count -= bits_consumed;
            exponent = exponent.saturating_sub(bits_consumed);
            break 'x;
        } else {
            let bits_consumed = bit_count;
            bits = rng();
            bit_count = 32;
            exponent = exponent.saturating_sub(bits_consumed);
            continue 'x;

    // if the caller requested a sign bit then compute that.
    let sign_bit: u32 = if SIGNED {
        if bit_count == 0 {
            ((rng() as i32) < 0) as u32
        } else {
            bits & 1
    } else {

    let out = f32::from_bits((sign_bit << SIGN_SHIFT) | (exponent << EXPONENT_SHIFT) | mantissa);

Seeding Your Generator

The process of picking the initial data bits for your generator is called “seeding” the generator.

Sometimes you might want to use a specific seed to re-create a specific situation. This isn’t complicated, you just set the generator state to whatever intensional seed you want.

The rest of the time you don’t have a specific seed in mind, and you usually want a totally unpredictable seed.

I’m not normally one to suggest that you use a crate, but the getrandom crate is a quick and effective solution. It takes a &mut [u8] to fill and then puts random bytes in there. The details depend on the OS, but it will usually call out to some sort of CSPRNG to get you your bytes. Regardless of exactly how big your generator is, you can get however many random bytes and set all the fields to random data.

Bonus Generator: wyrand

We’ve seen the LCG and PCG stuff, but there’s many more generators out there as well.

One generator that’s interesting is called “wyrand”. It’s part of the wyhash library. The basic generator, when converted to Rust, looks something like this:

/// returns (next_state, output)
pub fn wyrand(state: u64) -> (u64, u64) {
    let next_state = state.wrapping_add(0xA0761D6478BD642F);
    let x = state ^ 0xE7037ED1A0B428DB;
    let t = (state as u128).wrapping_mul(x as u128);
    let output = ((t >> 64) as u64) ^ (t as u64);
    (next_state, output)

It’s not entirely dissimilar from a PCG. There’s a basic state advance, and then we improve that state with a permutation to form the final output.

What makes this very interesting is that the state advance is a single operation. When something is just a single operation, that means we can do it atomically. This lets us build a global PRNG that’s contention free. Here’s a minimal example.

use core::sync::atomic::AtomicU64;
use core::sync::atomic::Ordering;

static GLOBAL_WYRAND: AtomicU64 = AtomicU64::new(12345);

pub fn global_wyrand_set(new_state: u64) {
    GLOBAL_WYRAND.store(new_state, Ordering::Relaxed);
pub fn global_wyrand_next_u64() -> u64 {
    let s = GLOBAL_WYRAND.fetch_add(0xA0761D6478BD642F, Ordering::Relaxed);
    let x = s ^ 0xE7037ED1A0B428DB;
    let t = (s as u128).wrapping_mul(x as u128);
    ((t >> 64) as u64) ^ (t as u64)

Wrap Up

That’s all for today. You hopefully have a handle on the basics of PRNG stuff.

Here’s the playground link that should hold all the major code blocks in the article. Assuming I didn’t forget anything.

Now everyone go out and flood the market with all of your own custom randomness crates.