Package java.util.random

This package contains classes and interfaces that support a generic API for random number generation.

These classes and interfaces support the definition and use of "random generators", a term covering what have traditionally been called "random number generators" as well as generators of other sorts of randomly chosen values (eg. booleans). These classes and interfaces cover not only deterministic (pseudorandom) algorithms but also generators of values that use some "truly random" physical source (stochastic algorithms perhaps making use of thermal noise, for example, or quantum-mechanical effects).

The principal interface is RandomGenerator, which provides methods for requesting individual values of type int, long, float, double, or boolean chosen pseudorandomly from a uniform distribution; methods for requesting values of type double chosen pseudorandomly from a normal distribution or from an exponential distribution; and methods for creating streams of values of type int, long, or double chosen pseudorandomly from a uniform distribution (such streams are spliterator-based, allowing for parallel processing of their elements). There are also static factory methods for creating an instance of a specific random number generator algorithm given its name.

The principal supporting class is RandomGeneratorFactory. This can be used to generate multiple random number generators for a specific algorithm. RandomGeneratorFactory also provides methods for selecting random number generator algorithms. RandomGeneratorFactory registers implementations of RandomGenerator interface using the service provider API.

An important subsidiary interface is RandomGenerator.StreamableGenerator, which provides methods for creating spliterator-based streams of RandomGenerator objects, allowing for parallel processing of these objects using multiple threads. Unlike Random, most implementations of RandomGenerator are not thread-safe. The intent is that instances should not be shared among threads; rather, each thread should have its own random generator(s) to use. The various pseudorandom algorithms provided by this package are designed so that multiple instances will (with very high probability) behave as if statistically independent.

For many purposes, these are the only two interfaces that a consumer of pseudorandom values will need. There are also some more specialized interfaces that describe more specialized categories of random number generators SplittableGenerator, JumpableGenerator, LeapableGenerator, and ArbitrarilyJumpableGenerator that have specific strategies for creating statistically independent instances.

Using the Random Number Generator Interfaces

To get started, an application should first create one instance of a generator class. Assume that the contents of the package java.util.random has been imported:

import java.util.random.*;

Then one can choose a specific implementation by giving the name of a generator algorithm to the static method RandomGenerator.of(java.lang.String), in which case the no-arguments constructor for that implementation is used:

RandomGenerator g = RandomGenerator.of("L64X128MixRandom");

For a single-threaded application, this is all that is needed. One can then invoke methods of g such as nextLong(), nextInt(), nextFloat(), nextDouble() and nextBoolean() to generate individual randomly chosen values. One can also use the methods ints(), longs() and doubles() to create streams of randomly chosen values. The methods nextGaussian() and nextExponential() draw floating-point values from nonuniform distributions.

For a multi-threaded application, one can repeat the preceding steps to create additional RandomGenerators, but often it is preferable to use methods of the one single initially created generator to create others like it. (One reason is that some generator algorithms, if asked to create a new set of generators all at once, can make a special effort to ensure that the new generators are statistically independent.) If the initial generator implements the interface RandomGenerator.StreamableGenerator, then the method rngs() can be used to create a stream of generators. If this is a parallel stream, then it is easy to get parallel execution by using the map() method on the stream.

For a multi-threaded application that forks new threads dynamically, another approach is to use an initial generator that implements the interface RandomGenerator.SplittableGenerator, which is then considered to "belong" to the initial thread for its exclusive use; then whenever any thread needs to fork a new thread, it first uses the split() method of its own generator to create a new generator, which is then passed to the newly created thread for exclusive use by that new thread.

Choosing a Random Number Generator Algorithm

There are three groups of random number generator algorithm provided in Java: the Legacy group, the LXM group, and the Xoroshiro/Xoshiro group.

The legacy group includes random number generators that existed before JDK 17: Random, ThreadLocalRandom, SplittableRandom, and SecureRandom. Random (LCG) is the weakest of the available algorithms, and it is recommended that users migrate to newer algorithms. If an application requires a random number generator algorithm that is cryptographically secure, then it should continue to use an instance of the class SecureRandom.

The algorithms in the LXM group are similar to each other. The parameters of each algorithm can be found in the algorithm name. The number after "L" indicates the number of state bits for the LCG subgenerator, and the number after "X" indicates the number of state bits for the XBG subgenerator. "Mix" indicates that the algorithm uses an 8-operation bit-mixing function; "StarStar" indicates use of a 3-operation bit-scrambler.

The algorithms in the Xoroshiro/Xoshiro group are more traditional algorithms (see David Blackman and Sebastiano Vigna, "Scrambled Linear Pseudorandom Number Generators," ACM Transactions on Mathematical Software, 2021); the number in the name indicates the number of state bits.

For applications (such as physical simulation, machine learning, and games) that do not require a cryptographically secure algorithm, this package provides multiple implementations of interface RandomGenerator that provide trade-offs among speed, space, period, accidental correlation, and equidistribution properties.

For applications with no special requirements, L64X128MixRandom has a good balance among speed, space, and period, and is suitable for both single-threaded and multi-threaded applications when used properly (a separate instance for each thread).

If the application uses only a single thread, then Xoroshiro128PlusPlus is even smaller and faster, and certainly has a sufficiently long period.

For an application running in a 32-bit hardware environment and using only one thread or a small number of threads, L32X64MixRandom may be a good choice.

For an application that uses many threads that are allocated in one batch at the start of the computation, either a "jumpable" generator such as Xoroshiro128PlusPlus or Xoshiro256PlusPlus may be used, or a "splittable" generator such as L64X128MixRandom or L64X256MixRandom may be used.

For an application that creates many threads dynamically, perhaps through the use of spliterators, a "splittable" generator such as L64X128MixRandom or L64X256MixRandom is recommended. If the number of generators created dynamically may be very large (millions or more), then using generators such as L128X128MixRandom or L128X256MixRandom, which use a 128-bit parameter rather than a 64-bit parameter for their LCG subgenerator, will make it much less likely that two instances use the same state cycle.

For an application that uses tuples of consecutively generated values, it may be desirable to use a generator that is k-equidistributed such that k is at least as large as the length of the tuples being generated. The generator L64X256MixRandom is provably 4-equidistributed, and L64X1024MixRandom is provably 16-equidistributed.

For applications that generate large permutations, it may be best to use a generator whose period is much larger than the total number of possible permutations; otherwise it will be impossible to generate some of the intended permutations. For example, if the goal is to shuffle a deck of 52 cards, the number of possible permutations is 52! (52 factorial), which is larger than 2²²⁵ (but smaller than 2²²⁶), so it may be best to use a generator whose period at least 2²⁵⁶, such as L64X256MixRandom or L64X1024MixRandom or L128X256MixRandom or L128X1024MixRandom. (It is of course also necessary to provide sufficiently many seed bits when the generator is initialized, or else it will still be impossible to generate some of the intended permutations.)

Random Number Generator Algorithms Available

These algorithms [in the table below] must be found with the current version of Java SE. A particular JDK implementation may recognize additional algorithms; check the JDK's documentation for details. The set of algorithms required by Java SE may be updated by changes to the Java SE specification. Over time, new algorithms may be added and old algorithms may be removed.

In addition, as another life-cycle phase, an algorithm may be deprecated. A deprecated algorithm is not recommended for use. If a required algorithm is deprecated, it may be removed in a future release. Due to advances in random number generator algorithm development and analysis, an algorithm may be deprecated during the lifetime of a particular Java SE release. Changing the deprecation status of an algorithm is not a specification change.

Available Algorithms

Algorithm	Group	Period	StateBits	Equidistribution
L128X1024MixRandom	LXM	BigInteger.ONE.shiftLeft(1024).subtract(BigInteger.ONE).shiftLeft(128)	1152	1
L128X128MixRandom	LXM	BigInteger.ONE.shiftLeft(128).subtract(BigInteger.ONE).shiftLeft(128)	256	1
L128X256MixRandom	LXM	BigInteger.ONE.shiftLeft(256).subtract(BigInteger.ONE).shiftLeft(128)	384	1
L32X64MixRandom	LXM	BigInteger.ONE.shiftLeft(64).subtract(BigInteger.ONE).shiftLeft(32)	96	1
L64X1024MixRandom	LXM	BigInteger.ONE.shiftLeft(1024).subtract(BigInteger.ONE).shiftLeft(64)	1088	16
L64X128MixRandom	LXM	BigInteger.ONE.shiftLeft(128).subtract(BigInteger.ONE).shiftLeft(64)	192	2
L64X128StarStarRandom	LXM	BigInteger.ONE.shiftLeft(128).subtract(BigInteger.ONE).shiftLeft(64)	192	2
L64X256MixRandom	LXM	BigInteger.ONE.shiftLeft(256).subtract(BigInteger.ONE).shiftLeft(64)	320	4
Random	Legacy	BigInteger.ONE.shiftLeft(48)	48	0
SplittableRandom	Legacy	BigInteger.ONE.shiftLeft(64)	64	1
ThreadLocalRandom *	Legacy	BigInteger.ONE.shiftLeft(64)	64	1
Xoroshiro128PlusPlus	Xoroshiro	BigInteger.ONE.shiftLeft(128).subtract(BigInteger.ONE)	128	1
Xoshiro256PlusPlus	Xoshiro	BigInteger.ONE.shiftLeft(256).subtract(BigInteger.ONE)	256	3

^* ThreadLocalRandom can only be accessed via ThreadLocalRandom.current().

Categories of Random Number Generator Algorithms

Historically, most pseudorandom generator algorithms have been based on some sort of finite-state machine with a single, large cycle of states; when it is necessary to have multiple threads use the same algorithm simultaneously, the usual technique is to arrange for each thread to traverse a different region of the state cycle. These regions may be doled out to threads by starting with a single initial state and then using a "jump function" that travels a long distance around the cycle (perhaps 2⁶⁴ steps or more); the jump function is applied repeatedly and sequentially, to identify widely spaced states that are then doled out, one to each thread, to serve as the initial state for the generator to be used by that thread. This strategy is supported by the interface RandomGenerator.JumpableGenerator. Sometimes it is desirable to support two levels of jumping (by long distances and by really long distances); this strategy is supported by the interface RandomGenerator.LeapableGenerator. There is also an interface RandomGenerator.ArbitrarilyJumpableGenerator for algorithms that allow jumping along the state cycle by any user-specified distance. In this package, implementations of these interfaces include "Xoroshiro128PlusPlus", and "Xoshiro256PlusPlus".

A more recent category of "splittable" pseudorandom generator algorithms uses a large family of state cycles and makes some attempt to ensure that distinct instances use different state cycles; but even if two instances "accidentally" use the same state cycle, they are highly likely to traverse different regions parts of that shared state cycle. This strategy is supported by the interface RandomGenerator.SplittableGenerator. In this package, implementations of this interface include "L32X64MixRandom", "L64X128StarStarRandom", "L64X128MixRandom", "L64X256MixRandom", "L64X1024MixRandom", "L128X128MixRandom", "L128X256MixRandom", and "L128X1024MixRandom"; note that the class SplittableRandom also implements this interface.

The LXM Family of Random Number Generator Algorithms

The structure of the central nextLong (or nextInt) method of an LXM algorithm follows a suggestion in December 2017 by Sebastiano Vigna that using one Linear Congruential Generator (LCG) as a first subgenerator and one Xor-Based Generator (XBG) as a second subgenerator (rather than using two LCG subgenerators) would provide a longer period, superior equidistribution, scalability, and better quality. Each of the specific implementations here combines one of the best currently known XBG algorithms (xoroshiro128 or xoshiro256, described by Blackman and Vigna in "Scrambled Linear Pseudorandom Number Generators", ACM Transactions on Mathematical Software, 2021) with an LCG that uses one of the best currently known multipliers (found by a search for better multipliers in 2019 by Steele and Vigna), and then applies either a mixing function identified by Doug Lea or a simple scrambler proposed by Blackman and Vigna. Testing has confirmed that the LXM algorithm is far superior in quality to the SplitMix algorithm (2014) used by SplittableRandom. Each class with a name of the form LpXqSomethingRandom uses some specific member of the LXM family of random number algorithms; "LXM" is short for "LCG, XBG, Mixer". Every LXM generator has two subgenerators; one is an LCG (Linear Congruential Generator) and the other is an XBG (Xor-Based Generator). Each output of an LXM generator is the result of combining state from the LCG with state from the XBG using a Mixing function (and then the state of the LCG and the state of the XBG are advanced).

The LCG subgenerator has an update step of the form s = m*s + a, where s, m, and a are all binary integers of the same size, each having p bits; s is the mutable state, the multiplier m is fixed (the same for all instances of a class) and the addend a is a parameter (a final field of the instance). The parameter a is required to be odd (this allows the LCG to have the maximal period, namely 2^p); therefore there are 2^p−1 distinct choices of parameter. (When the size of s is 128 bits, then we use the name "sh" below to refer to the high half of s, that is, the high-order 64 bits of s.)

The XBG subgenerator can in principle be any one of a wide variety of XBG algorithms; in this package it is always either xoroshiro128, xoshiro256, or xoroshiro1024, in each case without any final scrambler (such as "+" or "**") because LXM uses a separate Mixer later in the process. The XBG state consists of some fixed number of int or long fields, generally named x0, x1, and so on, which can take on any values provided that they are not all zero. The collective total size of these fields is q bits; therefore the period of this subgenerator is 2^q−1.

Because the periods 2^p and 2^q−1 of the two subgenerators are relatively prime, the period of any single instance of an LXM algorithm (the length of the series of generated values before it repeats) is the product of the periods of the subgenerators, that is, 2^p(2^q−1), which is just slightly smaller than 2^(p+q). Moreover, if two distinct instances of the same LXM algorithm have different a parameters, then their cycles of produced values will be different.

Generally speaking, among the "LpXq" generators, the memory required for an instance is 2p+q bits. (If q is 1024 or larger, the XBG state is represented as an array, so additional bits are needed for the array object header, and another 32 bits are used for an array index.)

Larger values of p imply a lower probability that two distinct instances will traverse the same state cycle, and larger values of q imply that the generator is equidistributed in a larger number of dimensions (this is provably true when p is 64, and conjectured to be approximately true when p is 128). A class with "Mix" in its name uses a fairly strong mixing function with excellent avalanche characteristics; a class with "StarStar" in its name uses a weaker but faster mixing function.

The specific LXM algorithms used in this package are all chosen so that the 64-bit values produced by the nextLong() method are exactly equidistributed (for example, for any specific instance of "L64X128MixRandom", over the course of its cycle each of the 2⁶⁴ possible long values will be produced 2¹²⁸−1 times). The values produced by the nextInt(), nextFloat(), and nextDouble() methods are likewise exactly equidistributed. Some algorithms provide a further guarantee of k-equidistribution for some k greater than 1, meaning that successive non-overlapping k-tuples of 64-bit values produced by the nextLong() method are exactly equidistributed (equally likely to occur).

The following table gives the period, state size (in bits), parameter size (in bits, including the low-order bit that is required always to be a 1-bit), and equidistribution property for each of the specific LXM algorithms used in this package.

Algorithm Properties

Implementation	Period	State size	Parameter size	nextLong() values are
"L32X64MixRandom"	232(264−1)	96 bits	32 bits
"L64X128StarStarRandom"	264(2128−1)	192 bits	64 bits	2-equidistributed and exactly equidistributed
"L64X128MixRandom"	264(2128−1)	192 bits	64 bits	2-equidistributed and exactly equidistributed
"L64X256MixRandom"	264(2256−1)	320 bits	64 bits	4-equidistributed and exactly equidistributed
"L64X1024MixRandom"	264(21024−1)	1088 bits	64 bits	16-equidistributed and exactly equidistributed
"L128X128MixRandom"	2128(2128−1)	256 bits	128 bits	exactly equidistributed
"L128X256MixRandom"	2128(2256−1)	384 bits	128 bits	exactly equidistributed
"L128X1024MixRandom"	2128(21024−1)	1152 bits	128 bits	exactly equidistributed

For the algorithms listed above whose names begin with L32, the 32-bit values produced by the nextInt() method are exactly equidistributed, but the 64-bit values produced by the nextLong() method are not exactly equidistributed.

For the algorithms listed above whose names begin with L64 or L128, the 64-bit values produced by the nextLong() method are exactly equidistributed: every instance, over the course of its cycle, will produce each of the 2⁶⁴ possible long values exactly the same number of times. For example, any specific instance of "L64X256MixRandom", over the course of its cycle each of the 2⁶⁴ possible long values will be produced 2²⁵⁶−1 times. The values produced by the nextInt(), nextFloat(), and nextDouble() methods are likewise exactly equidistributed.

In addition, for the algorithms listed above whose names begin with L64, the 64-bit values produced by the nextLong() method are k-equidistributed (but not exactly k-equidistributed). To be precise, and taking "L64X256MixRandom" as an example: for any specific instance of "L64X256MixRandom", consider the (overlapping) length-4 subsequences of the cycle of 64-bit values produced by nextLong() (assuming no other methods are called that would affect the state). There are 2⁶⁴(2²⁵⁶−1) such subsequences, and each subsequence, which consists of 4 64-bit values, can have one of 2²⁵⁶ values. Of those 2²⁵⁶ subsequence values, nearly all of them (2²⁵⁶−2⁶⁴) occur 2⁶⁴ times over the course of the entire cycle, and the other 2⁶⁴ subsequence values occur only 2⁶⁴−1 times. So the ratio of the probability of getting any specific one of the less common subsequence values and the probability of getting any specific one of the more common subsequence values is 1−2^-64. (Note that the set of 2⁶⁴ less-common subsequence values will differ from one instance of "L64X256MixRandom" to another, as a function of the additive parameter of the LCG.) The values produced by the nextInt(), nextFloat(), and nextDouble() methods are likewise 4-equidistributed (but not exactly 4-equidistributed).

The next table gives the LCG multiplier value, the name of the specific XBG algorithm used, the specific numeric parameters for that XBG algorithm, and the mixing function for each of the specific LXM algorithms used in this package. (Note that the multiplier used for the 128-bit LCG cases is 65 bits wide, so the constant 0x1d605bbb58c8abbfdL shown in the table cannot actually be used in code; instead, only the 64 low-order bits 0xd605bbb58c8abbfdL are represented in the source code, and the missing 1-bit is handled through special coding of the multiply-add algorithm used in the LCG.)

LXM Multipliers

Implementation	LCG multiplier m	XBG algorithm	XBG parameters	Mixing function
"L32X64MixRandom"	0xadb4a92d	xoroshiro64, version 1.0	(26, 9, 13)	mixLea32(s+x0)
"L64X128StarStarRandom"	0xd1342543de82ef95L	xoroshiro128, version 1.0	(24, 16, 37)	Long.rotateLeft((s+x0)* 5, 7) * 9
"L64X128MixRandom"	0xd1342543de82ef95L	xoroshiro128, version 1.0	(24, 16, 37)	mixLea64(s+x0)
"L64X256MixRandom"	0xd1342543de82ef95L	xoshiro256, version 1.0	(17, 45)	mixLea64(s+x0)
"L64X1024MixRandom"	0xd1342543de82ef95L	xoroshiro1024, version 1.0	(25, 27, 36)	mixLea64(s+x0)
"L128X128MixRandom"	0x1d605bbb58c8abbfdL	xoroshiro128, version 1.0	(24, 16, 37)	mixLea64(sh+x0)
"L128X256MixRandom"	0x1d605bbb58c8abbfdL	xoshiro256, version 1.0	(17, 45)	mixLea64(sh+x0)
"L128X1024MixRandom"	0x1d605bbb58c8abbfdL	xoroshiro1024, version 1.0	(25, 27, 36)	mixLea64(sh+x0)