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package java.util;

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

 *

 * 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

 *

 * <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

 *

 * </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}

 *

 * @author  Guy Steele

 * @author  Doug Lea

 * @since   1.8

 */

    /*

     * 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;

    }

    /**

     * Computes Stafford variant 13 of 64bit mix function.

     */

    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.

     */

    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.

     */

    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.

     */

    /* ---------------- 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()

        this.seed = mix64(s);

        this.gamma = mixGamma(s + GOLDEN_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()));

    }

    }

    /**

     * 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());

    }

    }

}