/*
* Copyright (c) 2013, 2019, Oracle and/or its affiliates. All rights reserved.
* ORACLE PROPRIETARY/CONFIDENTIAL. Use is subject to license terms.
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*/
// package java.util;
import java.math.BigInteger;
import java.util.concurrent.atomic.AtomicLong;
/**
* A generator of uniform pseudorandom values applicable for use in
* (among other contexts) isolated parallel computations that may
* generate subtasks. Class {@code SplittableRandom} supports methods for
* producing pseudorandom numbers of type {@code int}, {@code long},
* and {@code double} with similar usages as for class
* {@link java.util.Random} but differs in the following ways:
*
* <ul>
*
* <li>Series of generated values pass the DieHarder suite testing
* independence and uniformity properties of random number generators.
* (Most recently validated with <a
* href="http://www.phy.duke.edu/~rgb/General/dieharder.php"> version
* 3.31.1</a>.) These tests validate only the methods for certain
* types and ranges, but similar properties are expected to hold, at
* least approximately, for others as well. The <em>period</em>
* (length of any series of generated values before it repeats) is at
* least 2<sup>64</sup>. </li>
*
* <li> Method {@link #split} constructs and returns a new
* SplittableRandom instance that shares no mutable state with the
* current instance. However, with very high probability, the
* values collectively generated by the two objects have the same
* statistical properties as if the same quantity of values were
* generated by a single thread using a single {@code
* SplittableRandom} object. </li>
*
* <li>Instances of SplittableRandom are <em>not</em> thread-safe.
* They are designed to be split, not shared, across threads. For
* example, a {@link java.util.concurrent.ForkJoinTask
* fork/join-style} computation using random numbers might include a
* construction of the form {@code new
* Subtask(aSplittableRandom.split()).fork()}.
*
* <li>This class provides additional methods for generating random
* streams, that employ the above techniques when used in {@code
* stream.parallel()} mode.</li>
*
* </ul>
*
* <p>Instances of {@code SplittableRandom} are not cryptographically
* secure. Consider instead using {@link java.security.SecureRandom}
* in security-sensitive applications. Additionally,
* default-constructed instances do not use a cryptographically random
* seed unless the {@linkplain System#getProperty system property}
* {@code java.util.secureRandomSeed} is set to {@code true}.
*
* @author Guy Steele
* @author Doug Lea
* @since 1.8
*/
public final class SplittableRandom extends AbstractSplittableRng {
/*
* Implementation Overview.
*
* This algorithm was inspired by the "DotMix" algorithm by
* Leiserson, Schardl, and Sukha "Deterministic Parallel
* Random-Number Generation for Dynamic-Multithreading Platforms",
* PPoPP 2012, as well as those in "Parallel random numbers: as
* easy as 1, 2, 3" by Salmon, Morae, Dror, and Shaw, SC 2011. It
* differs mainly in simplifying and cheapening operations.
*
* The primary update step (method nextSeed()) is to add a
* constant ("gamma") to the current (64 bit) seed, forming a
* simple sequence. The seed and the gamma values for any two
* SplittableRandom instances are highly likely to be different.
*
* Methods nextLong, nextInt, and derivatives do not return the
* sequence (seed) values, but instead a hash-like bit-mix of
* their bits, producing more independently distributed sequences.
* For nextLong, the mix64 function is based on David Stafford's
* (http://zimbry.blogspot.com/2011/09/better-bit-mixing-improving-on.html)
* "Mix13" variant of the "64-bit finalizer" function in Austin
* Appleby's MurmurHash3 algorithm (see
* http://code.google.com/p/smhasher/wiki/MurmurHash3). The mix32
* function is based on Stafford's Mix04 mix function, but returns
* the upper 32 bits cast as int.
*
* The split operation uses the current generator to form the seed
* and gamma for another SplittableRandom. To conservatively
* avoid potential correlations between seed and value generation,
* gamma selection (method mixGamma) uses different
* (Murmurhash3's) mix constants. To avoid potential weaknesses
* in bit-mixing transformations, we restrict gammas to odd values
* with at least 24 0-1 or 1-0 bit transitions. Rather than
* rejecting candidates with too few or too many bits set, method
* mixGamma flips some bits (which has the effect of mapping at
* most 4 to any given gamma value). This reduces the effective
* set of 64bit odd gamma values by about 2%, and serves as an
* automated screening for sequence constant selection that is
* left as an empirical decision in some other hashing and crypto
* algorithms.
*
* The resulting generator thus transforms a sequence in which
* (typically) many bits change on each step, with an inexpensive
* mixer with good (but less than cryptographically secure)
* avalanching.
*
* The default (no-argument) constructor, in essence, invokes
* split() for a common "defaultGen" SplittableRandom. Unlike
* other cases, this split must be performed in a thread-safe
* manner, so we use an AtomicLong to represent the seed rather
* than use an explicit SplittableRandom. To bootstrap the
* defaultGen, we start off using a seed based on current time
* unless the java.util.secureRandomSeed property is set. This
* serves as a slimmed-down (and insecure) variant of SecureRandom
* that also avoids stalls that may occur when using /dev/random.
*
* It is a relatively simple matter to apply the basic design here
* to use 128 bit seeds. However, emulating 128bit arithmetic and
* carrying around twice the state add more overhead than appears
* warranted for current usages.
*
* File organization: First the non-public methods that constitute
* the main algorithm, then the main public methods, followed by
* some custom spliterator classes needed for stream methods.
*/
/**
* The golden ratio scaled to 64bits, used as the initial gamma
* value for (unsplit) SplittableRandoms.
*/
private static final long GOLDEN_GAMMA = 0x9e3779b97f4a7c15L;
/**
* The seed. Updated only via method nextSeed.
*/
private long seed;
/**
* The step value.
*/
private final long gamma;
/**
* Internal constructor used by all others except default constructor.
*/
private SplittableRandom(long seed, long gamma) {
this.seed = seed;
this.gamma = gamma;
}
/* The implementation of AbstractSplittableRng requires this. */
// SplittableRandom getThis() { return this; }
/**
* Computes Stafford variant 13 of 64bit mix function.
* http://zimbry.blogspot.com/2011/09/better-bit-mixing-improving-on.html
*/
private static long mix64(long z) {
z = (z ^ (z >>> 30)) * 0xbf58476d1ce4e5b9L;
z = (z ^ (z >>> 27)) * 0x94d049bb133111ebL;
return z ^ (z >>> 31);
}
/**
* Returns the 32 high bits of Stafford variant 4 mix64 function as int.
* http://zimbry.blogspot.com/2011/09/better-bit-mixing-improving-on.html
*/
private static int mix32(long z) {
z = (z ^ (z >>> 33)) * 0x62a9d9ed799705f5L;
return (int)(((z ^ (z >>> 28)) * 0xcb24d0a5c88c35b3L) >>> 32);
}
/**
* Returns the gamma value to use for a new split instance.
* Uses the 64bit mix function from MurmurHash3.
* https://github.com/aappleby/smhasher/wiki/MurmurHash3
*/
private static long mixGamma(long z) {
z = (z ^ (z >>> 33)) * 0xff51afd7ed558ccdL; // MurmurHash3 mix constants
z = (z ^ (z >>> 33)) * 0xc4ceb9fe1a85ec53L;
z = (z ^ (z >>> 33)) | 1L; // force to be odd
int n = Long.bitCount(z ^ (z >>> 1)); // ensure enough transitions
return (n < 24) ? z ^ 0xaaaaaaaaaaaaaaaaL : z;
}
/**
* Adds gamma to seed.
*/
private long nextSeed() {
return seed += gamma;
}
/**
* The seed generator for default constructors.
*/
private static final AtomicLong defaultGen = new AtomicLong(RngSupport.initialSeed());
/* ---------------- public methods ---------------- */
/**
* Creates a new SplittableRandom instance using the specified
* initial seed. SplittableRandom instances created with the same
* seed in the same program generate identical sequences of values.
*
* @param seed the initial seed
*/
public SplittableRandom(long seed) {
this(seed, GOLDEN_GAMMA);
}
/**
* Creates a new SplittableRandom instance that is likely to
* generate sequences of values that are statistically independent
* of those of any other instances in the current program; and
* may, and typically does, vary across program invocations.
*/
public SplittableRandom() { // emulate defaultGen.split()
long s = defaultGen.getAndAdd(2 * GOLDEN_GAMMA);
this.seed = mix64(s);
this.gamma = mixGamma(s + GOLDEN_GAMMA);
}
// public SplittableRandom copy() { return new SplittableRandom(seed, gamma); }
/**
* Constructs and returns a new SplittableRandom instance that
* shares no mutable state with this instance. However, with very
* high probability, the set of values collectively generated by
* the two objects has the same statistical properties as if the
* same quantity of values were generated by a single thread using
* a single SplittableRandom object. Either or both of the two
* objects may be further split using the {@code split()} method,
* and the same expected statistical properties apply to the
* entire set of generators constructed by such recursive
* splitting.
*
* @return the new SplittableRandom instance
*/
public SplittableRandom split() {
return new SplittableRandom(nextLong(), mixGamma(nextSeed()));
}
public SplittableRandom split(SplittableRng source) {
return new SplittableRandom(source.nextLong(), mixGamma(source.nextLong()));
}
/**
* Returns a pseudorandom {@code int} value.
*
* @return a pseudorandom {@code int} value
*/
public int nextInt() {
return mix32(nextSeed());
}
/**
* Returns a pseudorandom {@code long} value.
*
* @return a pseudorandom {@code long} value
*/
public long nextLong() {
return mix64(nextSeed());
}
static final BigInteger thePeriod = BigInteger.ONE.shiftLeft(64); // Period is 2**64
public BigInteger period() { return thePeriod; }
}