xrandom 0.1.1

All-purpose, rock-solid random number generators

xrandom

Classes that implement all-purpose, rock-solid random number generators.

Library priorities:

• perfect match of the numbers on all platforms
• performance
• quality of the numbers produced

Algorithms are Xoshiro for quality and Xorshift for speed.

Speed

Generating 50 million of random numbers with AOT-compiled binary.

Time (lower is better)	nextInt	nextDouble	nextBool
Random (dart:math)	1172	1541	1134
Xrandom	732	1122	718

Simplicity

It's compatible with the standard Random

import 'package:xrandom/xrandom.dart';

final random = Xrandom();

var a = random.nextBool(); 
var b = random.nextDouble();
var c = random.nextInt(n);

var unordered = [1, 2, 3, 4, 5]..shuffle(random);

Reproducibility

Xrandom's classes can also be created with expected method. It is made specifically for testing.

test('my test', () {
 final random = Xrandom.expected();
 // you'll get same sequence of numbers every time
 expect(random.nextInt(1000), 925);
 expect(random.nextInt(1000), 686);
 expect(random.nextInt(1000), 509); 
}); 

You can achieve the same determinism by creating the Random with a seed argument. However, this does not protect you from dart:math implementation updates.

The sequences produced by the expected() generators are intended to be reproducible.

(but not until the library reaches stable release status)

Aliases

Class	Is the same as	Works on
Xrandom	Xorshift32	Everywhere
Xrandom64	Xorshift128p	Everywhere except JavaScript

Xrandom64 is a more modern and advanced generator. On the other hand, Xrandom is extremely compatible and lightning fast.

Classes

Class	Arch	Algorithm	Algorithm author	Published
Xorshift32	32	xorshift32	G. Marsaglia	2003
Xorshift64	64	xorshift64	G. Marsaglia	2003
Xorshift128	32	xorshift128	G. Marsaglia	2003
Xorshift128p	64	xorshift128+	S. Vigna	2015
Xoshiro128pp	32	xoshiro128++ 1.0	D. Blackman and S. Vigna	2019
Xoshiro256pp	64	xoshiro256++ 1.0	D. Blackman and S. Vigna	2019

What to choose

Target	Mobile and Desktop	and JavaScript
Speed	Xorshift64	Xorshift32
Quality	Xoshiro256pp	Xoshiro128pp

JavaScript-enabled classes are always some trade-offs in favor of compatibility.

Compatibility

You can safely use any classes on mobile and desktop platforms.

However, if you also target JavaScript (Web, Node.js), you will have to limit the choice.

Full compatibility table:

Class	Is a	Mobile and Desktop	JavaScript
Xorshift32	32-bit	yes	yes
Xorshift128	32-bit	yes	yes
Xoshiro128pp	32-bit	yes	yes
Xorshift64	64-bit	yes	no
Xorshift128p	64-bit	yes	no
Xoshiro256pp	64-bit	yes	no

Speed optimizations

Raw bits

The nextInt32() and nextInt64() return the raw output of the generator.

Method	Returns	Equivalent of
nextInt32()	32-bit unsigned	nextInt(0xFFFFFFFE)+1
nextInt64()	64-bit signed	nextInt(0xFFFFFFFFFFFFFFFE)+1

Time (lower is better)	nextInt	nextInt32	nextInt64
Random (dart:math)	1172	-	-
Xorshift32	732	411	-
Xorshift64	1046	776	746
Xorshift128	886	606	-
Xorshift128p	1081	802	857
Xoshiro128pp	1203	902	-
Xoshiro256pp	1691	1405	1890

Rough double

nextFloat, unlike nextDouble, prefers speed to accuracy. It transforms a single 32-bit integer into a double. Therefore, the result is limited to a maximum of 2^32-1 values. But it's still a double with four billion shades.

Time (lower is better)	nextDouble	nextFloat
Random (dart:math)	1541	-
Xorshift32	1122	405
Xorshift64	994	777
Xorshift128	1443	588
Xorshift128p	1073	803
Xoshiro128pp	2015	904
Xoshiro256pp	2152	1386

More benchmarks

Time (lower is better)	nextInt	nextDouble	nextBool
Random (dart:math)	1172	1541	1134
Xorshift32	732	1122	718
Xorshift64	1046	994	703
Xorshift128	886	1443	729
Xorshift128p	1081	1073	715
Xoshiro128pp	1203	2015	736
Xoshiro256pp	1691	2152	744

All the benchmarks on this page are from AOT-compiled binaries running on AMD A9-9420e with Ubuntu 20.04. Time is measured in milliseconds.

Consistency

The library has been thoroughly tested to match reference numbers generated by C algorithms. The sources in C are taken directly from scientific publications or the reference implementations by the inventors of the algorithms. The Xorshift128+ results are also matched to reference values from JavaScript xorshift library, that tested the 128+ similarly.

Testing is done in the GitHub Actions cloud on Windows, Ubuntu and macOS in VM and Node.js modes.