Prospecting for Hash Functions
I recently got an itch to design my own noncryptographic integer hash function. Firstly, I wanted to better understand how hash functions work, and the best way to learn is to do. For years I’d been treating them like magic, shoving input into it and seeing randomlooking, but deterministic, output come out the other end. Just how is the avalanche effect achieved?
Secondly, could I apply my own particular strengths to craft a hash function better than the handful of functions I could find online? Especially the classic ones from Thomas Wang and Bob Jenkins. Instead of struggling with the mathematics, maybe I could software engineer my way to victory, working from the advantage of access to the excessive computational power of today.
Suppose, for example, I wrote tool to generate a random hash function definition, then JIT compile it to a native function in memory, then execute that function across various inputs to evaluate its properties. My tool could rapidly repeat this process in a loop until it stumbled upon an incredible hash function the world had never seen. That’s what I actually did. I call it the Hash Prospector:
https://github.com/skeeto/hashprospector
It only works on x8664 because it uses the same JIT compiling technique I’ve discussed before: allocate a page of memory, write some machine instructions into it, set the page to executable, cast the page pointer to a function pointer, then call the generated code through the function pointer.
Generating a hash function
My focus is on integer hash functions: a function that accepts an nbit integer and returns an nbit integer. One of the important properties of an integer hash function is that it maps its inputs to outputs 1:1. In other words, there are no collisions. If there’s a collision, then some outputs aren’t possible, and the function isn’t making efficient use of its entropy.
This is actually a lot easier than it sounds. As long as every nbit integer operation used in the hash function is reversible, then the hash function has this property. An operation is reversible if, given its output, you can unambiguously compute its input.
For example, XOR with a constant is trivially reversible: XOR the output with the same constant to reverse it. Addition with a constant is reversed by subtraction with the same constant. Since the integer operations are modular arithmetic, modulo 2^n for nbit integers, multiplication by an odd number is reversible. Odd numbers are coprime with the poweroftwo modulus, so there is some modular multiplicative inverse that reverses the operation.
Bret Mulvey’s hash function article provides a convenient list of some reversible operations available for constructing integer hash functions. This list was the catalyst for my little project. Here are the ones used by the hash prospector:
x = ~x;
x ^= constant;
x *= constant  1; // e.g. only odd constants
x += constant;
x ^= x >> constant;
x ^= x << constant;
x += x << constant;
x = x << constant;
x <<<= constant; // left rotation
I’ve come across a couple more useful operations while studying existing integer hash functions, but I didn’t put these in the prospector.
hash += ~(hash << constant);
hash = ~(hash << constant);
The prospector picks some operations at random and fills in their constants randomly within their proper constraints. For example, here’s an awful hash function I made it generate as an example:
// do NOT use this!
uint32_t
badhash32(uint32_t x)
{
x *= UINT32_C(0x1eca7d79);
x ^= x >> 20;
x = (x << 8)  (x >> 24);
x = ~x;
x ^= x << 5;
x += UINT32_C(0x10afe4e7);
return x;
}
That function is reversible, and it would be relatively straightforward to define its inverse. However, it has awful biases and poor avalanche. How do I know this?
The measure of a hash function
There are two key properties I’m looking for in randomly generated hash functions.

High avalanche effect. When I flip one input bit, the output bits should each flip with a 50% chance.

Low bias. Ideally there is no correlation between which output bits flip for a particular flipped input bit.
Initially I screwed up and only measured the first property. This lead to some hash functions that seemed to be amazing before close inspection, since, for a 32bit hash function, it was flipping over 15 output bits on average. However, the particular bits being flipped were heavily biased, resulting in obvious patterns in the output.
For example, when hashing a counter starting from zero, the high bits would follow a regular pattern. 15 to 16 bits were being flipped each time, but it was always the same bits.
Conveniently it’s easy to measure both properties at the same time. For an nbit integer hash function, create an n by n table initialized to zero. The rows are input bits and the columns are output bits. The ith row and jth column track the correlation between the ith input bit and jth output bit.
Then exhaustively iterate over all 2^n inputs, and flip each bit one at a time. Increment the appropriate element in the table if the output bit flips.
When you’re done, ideally each element in the table is exactly 2^(n1). That is, each output bit was flipped exactly half the time by each input bit. Therefore the bias of the hash function is the distance (the error) of the computed table from the ideal table.
For example, the ideal bias table for an 8bit hash function would be:
128 128 128 128 128 128 128 128
128 128 128 128 128 128 128 128
128 128 128 128 128 128 128 128
128 128 128 128 128 128 128 128
128 128 128 128 128 128 128 128
128 128 128 128 128 128 128 128
128 128 128 128 128 128 128 128
128 128 128 128 128 128 128 128
The hash prospector computes the standard deviation in order to turn this into a single, normalized measurement. Lower scores are better.
However, there’s still one problem: the input space for a 32bit hash function is over 4 billion values. The full test takes my computer about an hour and a half. Evaluating a 64bit hash function is right out.
Again, Monte Carlo to the rescue! Rather than sample the entire space, just sample a random subset. This provides a good estimate in less than a second, allowing lots of terrible hash functions to be discarded early. The full test can be saved only for the known good 32bit candidates. 64bit functions will only ever receive the estimate.
What did I find?
Once I got the bias issue sorted out, and after hours and hours of running, followed up with some manual tweaking on my part, the prospector stumbled across this little gem:
// DO use this one!
uint32_t
prospector32(uint32_t x)
{
x ^= x >> 15;
x *= UINT32_C(0x2c1b3c6d);
x ^= x >> 12;
x *= UINT32_C(0x297a2d39);
x ^= x >> 15;
return x;
}
According to a full (e.g. not estimated) bias evaluation, this function beats the snot out of most of 32bit hash functions I could find. It even comes out ahead of this well known hash function that I believe originates from the H2 SQL Database. (Update: Thomas Mueller has confirmed that, indeed, this is his hash function.)
uint32_t
hash32(uint32_t x)
{
x = ((x >> 16) ^ x) * UINT32_C(0x45d9f3b);
x = ((x >> 16) ^ x) * UINT32_C(0x45d9f3b);
x = (x >> 16) ^ x;
return x;
}
It’s still an excellent hash function, just slightly more biased than mine.
Very briefly, prospector32()
was the best 32bit hash function I could
find, and I thought I had a major breakthrough. Then I noticed the
finalizer function for the 32bit variant of MurmurHash3. It’s
also a 32bit hash function:
uint32_t
murmurhash32_mix32(uint32_t x)
{
x ^= x >> 16;
x *= UINT32_C(0x85ebca6b);
x ^= x >> 13;
x *= UINT32_C(0xc2b2ae35);
x ^= x >> 16;
return x;
}
This one is just barely less biased than mine. So I still haven’t discovered the best 32bit hash function, only the second best one. :)
A pattern emerges
If you’re paying close enough attention, you may have noticed that all three functions above have the same structure. The prospector had stumbled upon it all on its own without knowledge of the existing functions. It may not be so obvious for the second function, but here it is refactored:
uint32_t
hash32(uint32_t x)
{
x ^= x >> 16;
x *= UINT32_C(0x45d9f3b);
x ^= x >> 16;
x *= UINT32_C(0x45d9f3b);
x ^= x >> 16;
return x;
}
I hadn’t noticed this until after the prospector had come across it on its own. The pattern for all three is XORrightshift, multiply, XORrightshift, multiply, XORrightshift. There’s something particularly useful about this multiplyxorshift construction (also). The XORrightshift diffuses bits rightward and the multiply diffuses bits leftward. I like to think it’s “sloshing” the bits right, left, right, left.
It seems that multiplication is particularly good at diffusion, so it makes perfect sense to exploit it in noncryptographic hash functions, especially since modern CPUs are so fast at it. Despite this, it’s not used much in cryptography due to issues with completing it in constant time.
I like to think of this construction in terms of a fivetuple. For the three functions it’s the following:
(15, 0x2c1b3c6d, 12, 0x297a2d39, 15) // prospector32()
(16, 0x045d9f3b, 16, 0x045d9f3b, 16) // hash32()
(16, 0x85ebca6b, 13, 0xc2b2ae35, 16) // murmurhash32_mix32()
The prospector actually found lots of decent functions following this pattern, especially where the middle shift is smaller than the outer shift. Thinking of it in terms of this tuple, I specifically directed it to try different tuple constants. That’s what I meant by “tweaking.” Eventually my new function popped out with its really low bias.
The prospector has a template option (p
) if you want to try it
yourself:
$ ./prospector p xorr,mul,xorr,mul,xorr
If you really have your heart set on certain constants, such as my specific selection of shifts, you can lock those in while randomizing the other constants:
$ ./prospector p xorr:15,mul,xorr:12,mul,xorr:15
Or the other way around:
$ ./prospector p xorr,mul:2c1b3c6d,xorr,mul:297a2d39,xorr
My function seems a little strange using shifts of 15 bits rather than a nice, round 16 bits. However, changing those constants to 16 increases the bias. Similarly, neither of the two 32bit constants is a prime number, but nudging those constants to the nearest prime increases the bias. These parameters really do seem to be a local minima in the bias, and using prime numbers isn’t important.
What about 64bit integer hash functions?
So far I haven’t been able to improve on 64bit hash functions. The main function to beat is SplittableRandom / SplitMix64:
uint64_t
splittable64(uint64_t x)
{
x ^= x >> 30;
x *= UINT64_C(0xbf58476d1ce4e5b9);
x ^= x >> 27;
x *= UINT64_C(0x94d049bb133111eb);
x ^= x >> 31;
return x;
}
I also came across this one:
uint64_t
hash64(uint64_t x)
{
x ^= x >> 32;
x *= UINT64_C(0xd6e8feb86659fd93);
x ^= x >> 32;
x *= UINT64_C(0xd6e8feb86659fd93);
x ^= x >> 32;
return x;
}
Again, these follow the same construction as before. There really is something special about it, and many other people have noticed, too.
Both functions have about the same bias. (Remember, I can only estimate the bias for 64bit hash functions.) The prospector has found lots of functions with about the same bias, but nothing provably better. Until it does, I have no new 64bit integer hash functions to offer.
String hash
I’m also experimenting with using my hash function as a sort of primitive for a string hash function. Here I’m using my function in the loop to mix in one byte at a time, and finishing it with the same finalizer as MurmurHash3.
uint32_t
prospector32s(const void *buf, uint32_t len, uint32_t key)
{
uint32_t hash = key;
const unsigned char *p = buf;
for (uint32_t i = 0; i < len; i++) {
hash += p[i];
hash ^= hash >> 15;
hash *= UINT32_C(0x2c1b3c6d);
hash ^= hash >> 12;
hash *= UINT32_C(0x297a2d39);
hash ^= hash >> 15;
}
hash ^= len;
hash ^= hash >> 16;
hash *= UINT32_C(0x85ebca6b);
hash ^= hash >> 13;
hash *= UINT32_C(0xc2b2ae35);
hash ^= hash >> 16;
return hash + key;
}
It has the typical amount of collisions when running it on a large dictionary, so it seems decent enough but I don’t know if this hash function is worth much. More experimentation needed.
Beyond random search
Right now the prospector does a completely random, unstructured search hoping to stumble upon something good by chance. Perhaps it would be worth using a genetic algorithm to breed those 5tuples towards optimum? Others have had success in this area with simulated annealing.
There’s probably more to exploit from the multiplyxorshift construction that keeps popping up. If anything, the prospector is searching too broadly, looking at constructions that could never really compete no matter what the constants. In addition to everything above, I’ve been looking for good 32bit hash functions that don’t use any 32bit constants, but I’m really not finding any with a competitively low bias.
Update after one week
About one week after publishing this article I found an even better hash function. I believe this is the least biased 32bit integer hash function of this form ever devised. It’s even less biased than the MurmurHash3 finalizer.
// exact bias: 0.17353355999581582
uint32_t
lowbias32(uint32_t x)
{
x ^= x >> 16;
x *= UINT32_C(0x7feb352d);
x ^= x >> 15;
x *= UINT32_C(0x846ca68b);
x ^= x >> 16;
return x;
}
// inverse
uint32_t
lowbias32_r(uint32_t x)
{
x ^= x >> 16;
x *= UINT32_C(0x43021123);
x ^= x >> 15 ^ x >> 30;
x *= UINT32_C(0x1d69e2a5);
x ^= x >> 16;
return x;
}
If you’re willing to use an additional round of multiplyxorshift, this next function actually reaches the theoretical bias limit (bias = ~0.021) as exhibited by a perfect integer hash function:
// exact bias: 0.020888578919738908
uint32_t
triple32(uint32_t x)
{
x ^= x >> 17;
x *= UINT32_C(0xed5ad4bb);
x ^= x >> 11;
x *= UINT32_C(0xac4c1b51);
x ^= x >> 15;
x *= UINT32_C(0x31848bab);
x ^= x >> 14;
return x;
}
It’s statistically indistinguishable from a random permutation of all 32bit integers.
Update, February 2020
Some people have been experimenting with using my hash functions in GLSL shaders, and the results are looking good:
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