Statistical pseudo-random number generators for Motoko

May 27, 2026 · View on GitHub

Overview

The package provides multiple pseudo-random number generators.

Note: The PRNGs generate statistical pseudo-random numbers. They are not cryptographically secure.

Currently implemented generators:

Seiran128
SFC64, SFC32

Links

The package is published on Mops and GitHub. Please refer to the README on GitHub where it renders properly with formulas and tables.

API documentation: here on Mops

For updates, help, questions, feedback and other requests related to this package join us on:

Usage

Install with mops

You need mops installed. In your project directory run:

mops add prng

In the Motoko source file import the generators you need:

import { Seiran128; SFC64; SFC32 } "mo:prng";

(Importing the modules directly — e.g. import Seiran128 "mo:prng/Seiran128" — works too and is required for the rng.next() method-call syntax to resolve.)

Example

The two most commonly used generators from this package are Seiran128 and SFC64a. They both produce Nat64 output values. SFC64a is compatible with numpy.

import { Seiran128; SFC64 } "mo:prng";

let seed : Nat64 = 0;

let rng = Seiran128.new(seed);
let seq : [Nat64] = [rng.next(), rng.next()];

let rng2 = SFC64.SFC64a(seed);
let seq2 : [Nat64] = [rng2.next(), rng2.next()];

The seed argument is optional; omitting it uses each algorithm's default seed:

import { Seiran128; SFC64 } "mo:prng";

let rng = Seiran128.new(); // uses defaultSeiran128Seed
let rng2 = SFC64.SFC64a(); // uses defaultSFC64Seed

There are also two recommended Nat32 generators, SFC32a and SFC32b, used as follows.

import { SFC32 } "mo:prng";

let seed : Nat32 = 0;

let rng = SFC32.SFC32a(seed); // or SFC32.SFC32b(seed)
let seq : [Nat32] = [rng.next(), rng.next()];

For SFC the internal parameters of the generator can also be customized with a constructor like SFC64.new(24, 11, 3, seed). For more details take a look at the test files, the documentation in the source code, or https://mops.one/prng/docs.

Build & test

Run:

git clone git@github.com:research-ag/prng.git
cd prng
mops test

Formatting

To format the code, run:

npx -y prettier --plugin prettier-plugin-motoko --write '**/*.{mo,json,md}'

Benchmarks

Mops benchmark

Run

mops bench

Profiling

The benchmarks were produced with mops bench --replica dfx.

Time

Wasm instructions per invocation of next().

method	Seiran128	SFC64	SFC32
next	382	754	380

Memory

Heap allocation per invocation of next().

method	Seiran128	SFC64	SFC32
next	12	12	4