# peteroupc.github.io

## Notes on Jumping PRNGs Ahead

Peter Occil

Some pseudorandom number generators (PRNGs) have an efficient way to advance their state as though a huge number of PRNG outputs were discarded. Notes on how they work are described in the following sections.

### F2-linear PRNGs

For some PRNGs, each bit of the PRNG’s state can be described as a linear recurrence on its entire state. These PRNGs are called F2-linear PRNGs, and they include the following:

• Linear congruential generators (LCGs) with a power-of-two modulus.
• Xorshift PRNGs.
• PRNGs in the xoroshiro and xoshiro families.
• Linear or generalized feedback shift register generators, including Mersenne Twister.

For an F2-linear PRNG, there is an efficient way to discard a given (and arbitrary) number of its outputs (to “jump the PRNG ahead”). This jump-ahead strategy is further described in (Haramoto et al., 2008)1. See also (Vigna 2017)2. To calculate the jump-ahead parameters needed to advance the PRNG N steps:

1. Build `M`, an S×S matrix of zeros and ones that describes the linear transformation of the PRNG’s state, where S is the size of that state in bits. For an example, see sections 3.1 and 3.2 of (Blackman and Vigna 2019)3, where it should be noted that the additions inside the matrix are actually XORs.
2. Find the characteristic polynomial of `M`. This has to be done in the two-element field F2, so that each coefficient of the polynomial is either 0 or 1.

For example, SymPy’s `charpoly()` method alone is inadequate for this purpose, since it doesn’t operate on the correct field. However, it’s easy to adapt that method’s output for the field F2: even coefficients become zeros and odd coefficients become ones.

Note that for a linear feedback shift register (LFSR) generator, the characteristic polynomial’s coefficients are 1 for each of its “taps” (and “tap” 0), and 0 elsewhere. For example, an LFSR generator with taps 6 and 8 has the characteristic polynomial x8 + x6 + 1.

The section “Jump Parameters for Some PRNGs” shows characteristic polynomials for some PRNGs and one way their coefficients can be represented.

3. Calculate `powmodf2(2, N, CP)`, where `powmodf2` is a modular power function that calculates `2^N mod CP` in the field F2, and `CP` is the characteristic polynomial. Regular modular power functions, such as BigInteger’s `modPow` method, won’t work here, even if the polynomial is represented in the manner described in “Jump Parameters for Some PRNGs”.
4. The result is a jump polynomial for jumping the PRNG ahead N steps.

An example of its use is found in the `jump` and `long_jump` functions in the `xoroshiro128plus` source code, which are identical except for the jump polynomial. In both functions, the jump polynomial’s coefficients are packed into a 128-bit integer (as described in “Jump Parameters for Some PRNGs”), which is then split into the lower 64 bits and the upper 64 bits, in that order.

### Counter-Based PRNGs

Counter-based PRNGs, in which their state is updated simply by incrementing a counter, can be trivially jumped ahead just by changing the seed, the counter, or both (Salmon et al. 2011)4.

### Multiple Recursive Generators

A multiple recursive generator (MRG) generates numbers by transforming its state using the following formula: `x(k) = (x(k-1)*A(1) + x(k-2)*A(2) + ... + x(k-n)*A(n)) mod modulus`, where `A(i)` are the multipliers and `modulus` is the modulus.

For an MRG, the following matrix (`M`) describes the state transition `[x(k-n), ..., x(k-1)]` to `[x(k-n+1), ..., x(k)]` (mod `modulus`):

`````` | 0   1   0  ...  0  |
| 0   0   1  ...  0  |
| .   .   .  ... ... |
| 0   0   0  ...  1  |
|A(n)A(n A(n ... A(1)|
|     -1) -2)        |
``````

To calculate the parameter needed to jump the MRG ahead N steps, calculate `M`N mod `modulus`; the result is a jump matrix `J`.

Then, to jump the MRG ahead N steps, calculate `J * S` mod `modulus`, where `J` is the jump matrix and `S` is the state in the form of a column vector; the result is a new state for the MRG.

This technique was mentioned (but for binary matrices) in Haramoto, in sections 1 and 3.1. They point out, though, that it isn’t efficient if the transition matrix is large. See also (L’Ecuyer et al., 2002)5.

#### Example

A multiple recursive generator with a modulus of 1449 has the following transition matrix:

``````|  0   1   0  |
|  0   0   1  |
| 444 342 499 |
``````

To calculate the 3×3 jump matrix to jump 100 steps from this MRG, raise this matrix to the power of 100 then reduce the result’s elements mod 1449. One way to do this is the “square-and-multiply” method, described by D. Knuth in The Art of Computer Programming: Set J to the identity matrix, N to 100, and M to a copy of the transition matrix, then while N is greater than 0:

1. If N is odd6, multiply J by M then reduce J’s elements mod 1449.
2. Divide N by 2 and round down, then multiply M by M then reduce M’s elements mod 1449.

The resulting J is a jump matrix as follows:

``````| 156   93  1240 |
| 1389 1128  130 |
| 1209  930  793 |
``````

Transforming the MRG’s state with J (and reducing mod 1449) will transform the state as though 100 outputs were discarded from the MRG.

### Linear Congruential Generators

A linear congruential generator (LCG) generates numbers by transforming its state using the following formula: `x(k) = (x(k-1)*a + c) mod modulus`, where `a` is the multiplier, `c` is the additive constant, and `modulus` is the modulus.

An efficient way to jump an LCG ahead is described in (Brown 1994)7. This also applies to LCGs that transform each `x(k)` before outputting it, such as M.O’Neill’s PCG32 and PCG64.

An MRG with only one multiplier expresses the special case of an LCG with `c = 0` (also known as a multiplicative LCG). For `c` other than 0, the following matrix describes the state transition `[x(k-1), 1]` to `[x(k), 1]` (mod `modulus`):

`````` | a   c |
| 0   1 |
``````

Jumping the LCG ahead can then be done using this matrix as described in the previous section.

There are implementations for jumping a multiply-with-carry (MWC) PRNG ahead, but only in source code form (ref. 1). I am not aware of an article or paper that describes how jumping an MWC PRNG ahead works.

I am not aware of any efficient ways to jump an add-with-carry or subtract-with-borrow PRNG ahead an arbitrary number of steps.

### Combined PRNGs

A combined PRNG can be jumped ahead N steps by jumping each of its components ahead N steps.

### Jump Parameters for Some PRNGs

The following table shows the characteristic polynomial and jump polynomials for some PRNG families. In the table:

• Each polynomial’s coefficients are zeros and ones, so the table shows them as a base-16 integer that stores the coefficients as individual bits: the least significant bit is the degree-0 coefficient, the next bit is the degree-1 coefficient, and so on. For example, the integer 0x23 stores the coefficients of the polynomial x5 + x + 1.
• “‘Period’/φ” means the PRNG’s maximum cycle length divided by the golden ratio, and rounded to the closest odd integer; this jump parameter is chosen to avoid overlapping number sequences as much as possible (see also NumPy documentation).

PRNG Characteristic Polynomial Jump Polynomials
xoroshiro64 0x1053be9da6e2286c1 232: 0x4cbf99bd77fcd1a0
248: 0xb4e7e4633f1f8b95
“Period”/φ: 0x751f355609af0e3b
xoshiro128 0x100fc65a2006254b11b489db6de18fc01 232: 0xf8aed94730b948df3be07b8f7afe108
248: 0xdeaa4ca2dec5bb9a87a4583dcb56667c
264: 0x77f2db5b6fa035c3f542d2d38764000b
296: 0x1c580662ccf5a0ef0b6f099fb523952e
“Period”/φ: 0x338b58d0590169928fda8fd5d1cf96b6
xoroshiro128 (except ++) 0x10008828e513b43d5095b8f76579aa001 232: 0xd4e95eef9edbdbc6fad843622b252c78
264: 0x170865df4b3201fcdf900294d8f554a5
296: 0xdddf9b1090aa7ac1d2a98b26625eee7b
“Period”/φ: 0xc1c620fd7bf598c34a2828365a7df3e0
xoroshiro128++ 0x10031bcf2f855d6e58dae70779760b081 232: 0x2e1bcf52f1051044fcceec21d5c306d9
248: 0xc8462a08ab3d7f9b99030a888c867939
264: 0x992ccaf6a6fca052bd7a6a6e99c2ddc
296: 0x9c6e6877736c46e3360fd5f2cf8d5d99
“Period”/φ: 0x1b4c7a8989405b16d3e4e127a6a11513
xoshiro256 0x10003c03c3f3ecb1904b4edcf26259f85­0280002bcefd1a5e9d116f2bb0f0f001 232: 0xe055d3520fdb9d7214fafc0fbdbc2087d8d0632bd08e6ac58120d583c112f69
248: 0x5f728be2c97e9066474579292f705634f825539dee5e4763f11fb4faea62c7f1
264: 0x12e4a2fbfc19bff934faff184785c20ab60d6c5b8c78f106b13c16e8096f0754
296: 0x31eebb6c82a9615fb27c05962ea56a13cdb45d7def42c317148c356c3114b7a9