Andrew Kensler, from Pixar, introduced an interesting technique for generating the permutation of an array in his 2013 paper, Correlated Multi-Jittered Sampling.

Firstly, let’s look at the naive way of generating a permutation. You construct
an array of elements from , and then you randomly shuffle them.
Then, your resulting array (let’s call it `A`

), will have the permuted value
for `i`

at `A[i]`

.

```
from random import shuffle
n = 10
permutation = list(range(n))
permutation = shuffle(permutation)
```

The bright side of this is that it’s really easy to implement and fairly easy
to access. You simply subscript the array at whichever number you want to
permute and you get the permutation for that number. The downside is that it’s
`O(n)`

for space complexity and `O(n)`

for time complexity. At the very
least, you need to create an array of `n`

elements, yielding an `O(n)`

space
complexity, and then generate the array, which is `O(n)`

time, and then shuffle
the array, which is also `O(n)`

with the Fisher-Yates algorithm, which the
`shuffle`

method from Python uses.

Kensler’s paper introduces a way to turn this into `O(1)`

for both space and
time through some clever hashing techniques. He mentions some prior work in AES
which uses hashed permutations, and notes that any hash function that is
reversible must be a permutation so long as it remains within a defined domain.
Why? Consider that a hash is (ideally) a 1-1 mapping of elements. If we are
constrained to a domain, then that means that every element in a domain maps to
another element within the domain, and thus we end up with a unique mapping of
elements to other elements. Because the domain of the input set and output set
are the same, we have a random shuffling of the input set.

In Kensler’s paper, he defines a set of operations that are reversible in any domain that is a power of 2. Most of these are trivially reversible, and don’t forget that shifting a number to the left by one bit simply amounts to doubling the number, and shifting to the right is the same as dividing the number by two.

```
hash ^= constant;
hash *= constant; // if the constant is odd
hash += constant;
hash -= constant;
hash ^= hash >> constant;
hash ^= hash << constant;
hash += hash << constant;
hash -= hash << constant;
hash = (hash << constant) | (hash >> wordsize_constant);
```

Of course, you may be a little put off by the constraint that the domain must be a power of two, but Kensler shows how we can mitigate that to operate within arbitrary domains using cycle walking. Cycle walking is a technique in cryptography where you basically just repeatedly encrypt information until it falls within an acceptable range. In this case, we can continue applying permutations to a number until it falls within the proper range.

Let’s take a look at the actual permutation function.

```
/*!
* \brief Permute a number
*
* \param i The number to permute/the index of the permutation vector
* \param l The desired size of the permutation vector
* \param p The seed of the shuffle
*/
unsigned permute(unsigned i, unsigned l, unsigned p) {
unsigned w = l - 1;
w |= w >> 1;
w |= w >> 2;
w |= w >> 4;
w |= w >> 8;
w |= w >> 16;
do {
i ^= p;
i *= 0xe170893d;
i ^= p >> 16;
i ^= (i & w) >> 4;
i ^= p >> 8;
i *= 0x0929eb3f;
i ^= p >> 23;
i ^= (i & w) >> 1;
i *= 1 | p >> 27;
i *= 0x6935fa69;
i ^= (i & w) >> 11;
i *= 0x74dcb303;
i ^= (i & w) >> 2;
i *= 0x9e501cc3;
i ^= (i & w) >> 2;
i *= 0xc860a3df;
i &= w;
i ^= i >> 5;
} while (i >= l);
return (i + p) % l;
}
```

*This was taken from Kensler’s paper, the comments added to explain the
function signature are mine.*

This looks rather daunting, with a lot of bitwise operations, but remember that
the entire thing is comprised of operations we just discussed are being
reversible. This means that this is a hash operation that is a permutation, so
each number will unique map to another number within the smallest power of two
domain that is larger than `l`

.

The body of the function inside of the do-while loop is basically just applying
a permuted hash repeatedly until our number is within the domain, which we
define with `l`

. `p`

can be more or less treated as a random seed, it lets us
apply some arbitrary offset which retaining the uniqueness property of a
permutation. It’s fairly self explanatory: apply some offset and modulo it
within the domain of , and because the input domain is the same
as the output domain, we retain the 1-1 mapping and always get a proper
permutation.

You may also be wondering what’s going on with `w`

operations before the for
loop. The algorithm takes `i`

and returns the greatest number in the domain
that is less than a power of two. The “actual” domains in this case are powers
of two, and this operation yields the highest number that falls within that
domain. So if we have some number , find the smallest number of two
such that . The code gives us . We can also explain this
in terms of bits. It takes the leftmost bit, and turns all of the bits to the
right of the bit to `1`

. If the number is a power of two, then the leftmost bit
is flipped to a `0`

.

Using this function gives you a very low overhead way to generate particular
permutations or shuffles on the fly with `O(1)`

space and time complexity.