1 /* 2 * Copyright (c) 1995, 2011, Oracle and/or its affiliates. All rights reserved. 3 * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER. 4 * 5 * This code is free software; you can redistribute it and/or modify it 6 * under the terms of the GNU General Public License version 2 only, as 7 * published by the Free Software Foundation. Oracle designates this 8 * particular file as subject to the "Classpath" exception as provided 9 * by Oracle in the LICENSE file that accompanied this code. 10 * 11 * This code is distributed in the hope that it will be useful, but WITHOUT 12 * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or 13 * FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License 14 * version 2 for more details (a copy is included in the LICENSE file that 15 * accompanied this code). 16 * 17 * You should have received a copy of the GNU General Public License version 18 * 2 along with this work; if not, write to the Free Software Foundation, 19 * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA. 20 * 21 * Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA 22 * or visit www.oracle.com if you need additional information or have any 23 * questions. 24 */ 25 26 package java.util; 27 import java.io.*; 28 import java.util.concurrent.atomic.AtomicLong; 29 import java.util.stream.DoubleStream; 30 import java.util.stream.IntStream; 31 import java.util.stream.LongStream; 32 33 import sun.misc.Unsafe; 34 35 /** 36 * An instance of this class is used to generate a stream of 37 * pseudorandom numbers. The class uses a 48-bit seed, which is 38 * modified using a linear congruential formula. (See Donald Knuth, 39 * <i>The Art of Computer Programming, Volume 2</i>, Section 3.2.1.) 40 * <p> 41 * If two instances of {@code Random} are created with the same 42 * seed, and the same sequence of method calls is made for each, they 43 * will generate and return identical sequences of numbers. In order to 44 * guarantee this property, particular algorithms are specified for the 45 * class {@code Random}. Java implementations must use all the algorithms 46 * shown here for the class {@code Random}, for the sake of absolute 47 * portability of Java code. However, subclasses of class {@code Random} 48 * are permitted to use other algorithms, so long as they adhere to the 49 * general contracts for all the methods. 50 * <p> 51 * The algorithms implemented by class {@code Random} use a 52 * {@code protected} utility method that on each invocation can supply 53 * up to 32 pseudorandomly generated bits. 54 * <p> 55 * Many applications will find the method {@link Math#random} simpler to use. 56 * 57 * <p>Instances of {@code java.util.Random} are threadsafe. 58 * However, the concurrent use of the same {@code java.util.Random} 59 * instance across threads may encounter contention and consequent 60 * poor performance. Consider instead using 61 * {@link java.util.concurrent.ThreadLocalRandom} in multithreaded 62 * designs. 63 * 64 * <p>Instances of {@code java.util.Random} are not cryptographically 65 * secure. Consider instead using {@link java.security.SecureRandom} to 66 * get a cryptographically secure pseudo-random number generator for use 67 * by security-sensitive applications. 68 * 69 * @author Frank Yellin 70 * @since 1.0 71 */ 72 public 73 class Random implements java.io.Serializable { 74 /** use serialVersionUID from JDK 1.1 for interoperability */ 75 static final long serialVersionUID = 3905348978240129619L; 76 77 /** 78 * The internal state associated with this pseudorandom number generator. 79 * (The specs for the methods in this class describe the ongoing 80 * computation of this value.) 81 */ 82 private final AtomicLong seed; 83 84 private static final long multiplier = 0x5DEECE66DL; 85 private static final long addend = 0xBL; 86 private static final long mask = (1L << 48) - 1; 87 88 /** 89 * Creates a new random number generator. This constructor sets 90 * the seed of the random number generator to a value very likely 91 * to be distinct from any other invocation of this constructor. 92 */ 93 public Random() { 94 this(seedUniquifier() ^ System.nanoTime()); 95 } 96 97 private static long seedUniquifier() { 98 // L'Ecuyer, "Tables of Linear Congruential Generators of 99 // Different Sizes and Good Lattice Structure", 1999 100 for (;;) { 101 long current = seedUniquifier.get(); 102 long next = current * 181783497276652981L; 103 if (seedUniquifier.compareAndSet(current, next)) 104 return next; 105 } 106 } 107 108 private static final AtomicLong seedUniquifier 109 = new AtomicLong(8682522807148012L); 110 111 /** 112 * Creates a new random number generator using a single {@code long} seed. 113 * The seed is the initial value of the internal state of the pseudorandom 114 * number generator which is maintained by method {@link #next}. 115 * 116 * <p>The invocation {@code new Random(seed)} is equivalent to: 117 * <pre> {@code 118 * Random rnd = new Random(); 119 * rnd.setSeed(seed);}</pre> 120 * 121 * @param seed the initial seed 122 * @see #setSeed(long) 123 */ 124 public Random(long seed) { 125 if (getClass() == Random.class) 126 this.seed = new AtomicLong(initialScramble(seed)); 127 else { 128 // subclass might have overriden setSeed 129 this.seed = new AtomicLong(); 130 setSeed(seed); 131 } 132 } 133 134 private static long initialScramble(long seed) { 135 return (seed ^ multiplier) & mask; 136 } 137 138 /** 139 * Sets the seed of this random number generator using a single 140 * {@code long} seed. The general contract of {@code setSeed} is 141 * that it alters the state of this random number generator object 142 * so as to be in exactly the same state as if it had just been 143 * created with the argument {@code seed} as a seed. The method 144 * {@code setSeed} is implemented by class {@code Random} by 145 * atomically updating the seed to 146 * <pre>{@code (seed ^ 0x5DEECE66DL) & ((1L << 48) - 1)}</pre> 147 * and clearing the {@code haveNextNextGaussian} flag used by {@link 148 * #nextGaussian}. 149 * 150 * <p>The implementation of {@code setSeed} by class {@code Random} 151 * happens to use only 48 bits of the given seed. In general, however, 152 * an overriding method may use all 64 bits of the {@code long} 153 * argument as a seed value. 154 * 155 * @param seed the initial seed 156 */ 157 synchronized public void setSeed(long seed) { 158 this.seed.set(initialScramble(seed)); 159 haveNextNextGaussian = false; 160 } 161 162 /** 163 * Generates the next pseudorandom number. Subclasses should 164 * override this, as this is used by all other methods. 165 * 166 * <p>The general contract of {@code next} is that it returns an 167 * {@code int} value and if the argument {@code bits} is between 168 * {@code 1} and {@code 32} (inclusive), then that many low-order 169 * bits of the returned value will be (approximately) independently 170 * chosen bit values, each of which is (approximately) equally 171 * likely to be {@code 0} or {@code 1}. The method {@code next} is 172 * implemented by class {@code Random} by atomically updating the seed to 173 * <pre>{@code (seed * 0x5DEECE66DL + 0xBL) & ((1L << 48) - 1)}</pre> 174 * and returning 175 * <pre>{@code (int)(seed >>> (48 - bits))}.</pre> 176 * 177 * This is a linear congruential pseudorandom number generator, as 178 * defined by D. H. Lehmer and described by Donald E. Knuth in 179 * <i>The Art of Computer Programming,</i> Volume 3: 180 * <i>Seminumerical Algorithms</i>, section 3.2.1. 181 * 182 * @param bits random bits 183 * @return the next pseudorandom value from this random number 184 * generator's sequence 185 * @since 1.1 186 */ 187 protected int next(int bits) { 188 long oldseed, nextseed; 189 AtomicLong seed = this.seed; 190 do { 191 oldseed = seed.get(); 192 nextseed = (oldseed * multiplier + addend) & mask; 193 } while (!seed.compareAndSet(oldseed, nextseed)); 194 return (int)(nextseed >>> (48 - bits)); 195 } 196 197 /** 198 * Generates random bytes and places them into a user-supplied 199 * byte array. The number of random bytes produced is equal to 200 * the length of the byte array. 201 * 202 * <p>The method {@code nextBytes} is implemented by class {@code Random} 203 * as if by: 204 * <pre> {@code 205 * public void nextBytes(byte[] bytes) { 206 * for (int i = 0; i < bytes.length; ) 207 * for (int rnd = nextInt(), n = Math.min(bytes.length - i, 4); 208 * n-- > 0; rnd >>= 8) 209 * bytes[i++] = (byte)rnd; 210 * }}</pre> 211 * 212 * @param bytes the byte array to fill with random bytes 213 * @throws NullPointerException if the byte array is null 214 * @since 1.1 215 */ 216 public void nextBytes(byte[] bytes) { 217 for (int i = 0, len = bytes.length; i < len; ) 218 for (int rnd = nextInt(), 219 n = Math.min(len - i, Integer.SIZE/Byte.SIZE); 220 n-- > 0; rnd >>= Byte.SIZE) 221 bytes[i++] = (byte)rnd; 222 } 223 224 /** 225 * Returns the next pseudorandom, uniformly distributed {@code int} 226 * value from this random number generator's sequence. The general 227 * contract of {@code nextInt} is that one {@code int} value is 228 * pseudorandomly generated and returned. All 2<font size="-1"><sup>32 229 * </sup></font> possible {@code int} values are produced with 230 * (approximately) equal probability. 231 * 232 * <p>The method {@code nextInt} is implemented by class {@code Random} 233 * as if by: 234 * <pre> {@code 235 * public int nextInt() { 236 * return next(32); 237 * }}</pre> 238 * 239 * @return the next pseudorandom, uniformly distributed {@code int} 240 * value from this random number generator's sequence 241 */ 242 public int nextInt() { 243 return next(32); 244 } 245 246 /** 247 * Returns a pseudorandom, uniformly distributed {@code int} value 248 * between 0 (inclusive) and the specified value (exclusive), drawn from 249 * this random number generator's sequence. The general contract of 250 * {@code nextInt} is that one {@code int} value in the specified range 251 * is pseudorandomly generated and returned. All {@code n} possible 252 * {@code int} values are produced with (approximately) equal 253 * probability. The method {@code nextInt(int n)} is implemented by 254 * class {@code Random} as if by: 255 * <pre> {@code 256 * public int nextInt(int n) { 257 * if (n <= 0) 258 * throw new IllegalArgumentException("n must be positive"); 259 * 260 * if ((n & -n) == n) // i.e., n is a power of 2 261 * return (int)((n * (long)next(31)) >> 31); 262 * 263 * int bits, val; 264 * do { 265 * bits = next(31); 266 * val = bits % n; 267 * } while (bits - val + (n-1) < 0); 268 * return val; 269 * }}</pre> 270 * 271 * <p>The hedge "approximately" is used in the foregoing description only 272 * because the next method is only approximately an unbiased source of 273 * independently chosen bits. If it were a perfect source of randomly 274 * chosen bits, then the algorithm shown would choose {@code int} 275 * values from the stated range with perfect uniformity. 276 * <p> 277 * The algorithm is slightly tricky. It rejects values that would result 278 * in an uneven distribution (due to the fact that 2^31 is not divisible 279 * by n). The probability of a value being rejected depends on n. The 280 * worst case is n=2^30+1, for which the probability of a reject is 1/2, 281 * and the expected number of iterations before the loop terminates is 2. 282 * <p> 283 * The algorithm treats the case where n is a power of two specially: it 284 * returns the correct number of high-order bits from the underlying 285 * pseudo-random number generator. In the absence of special treatment, 286 * the correct number of <i>low-order</i> bits would be returned. Linear 287 * congruential pseudo-random number generators such as the one 288 * implemented by this class are known to have short periods in the 289 * sequence of values of their low-order bits. Thus, this special case 290 * greatly increases the length of the sequence of values returned by 291 * successive calls to this method if n is a small power of two. 292 * 293 * @param n the bound on the random number to be returned. Must be 294 * positive. 295 * @return the next pseudorandom, uniformly distributed {@code int} 296 * value between {@code 0} (inclusive) and {@code n} (exclusive) 297 * from this random number generator's sequence 298 * @throws IllegalArgumentException if n is not positive 299 * @since 1.2 300 */ 301 302 public int nextInt(int n) { 303 if (n <= 0) 304 throw new IllegalArgumentException("n must be positive"); 305 306 if ((n & -n) == n) // i.e., n is a power of 2 307 return (int)((n * (long)next(31)) >> 31); 308 309 int bits, val; 310 do { 311 bits = next(31); 312 val = bits % n; 313 } while (bits - val + (n-1) < 0); 314 return val; 315 } 316 317 /** 318 * Returns the next pseudorandom, uniformly distributed {@code long} 319 * value from this random number generator's sequence. The general 320 * contract of {@code nextLong} is that one {@code long} value is 321 * pseudorandomly generated and returned. 322 * 323 * <p>The method {@code nextLong} is implemented by class {@code Random} 324 * as if by: 325 * <pre> {@code 326 * public long nextLong() { 327 * return ((long)next(32) << 32) + next(32); 328 * }}</pre> 329 * 330 * Because class {@code Random} uses a seed with only 48 bits, 331 * this algorithm will not return all possible {@code long} values. 332 * 333 * @return the next pseudorandom, uniformly distributed {@code long} 334 * value from this random number generator's sequence 335 */ 336 public long nextLong() { 337 // it's okay that the bottom word remains signed. 338 return ((long)(next(32)) << 32) + next(32); 339 } 340 341 /** 342 * Returns the next pseudorandom, uniformly distributed 343 * {@code boolean} value from this random number generator's 344 * sequence. The general contract of {@code nextBoolean} is that one 345 * {@code boolean} value is pseudorandomly generated and returned. The 346 * values {@code true} and {@code false} are produced with 347 * (approximately) equal probability. 348 * 349 * <p>The method {@code nextBoolean} is implemented by class {@code Random} 350 * as if by: 351 * <pre> {@code 352 * public boolean nextBoolean() { 353 * return next(1) != 0; 354 * }}</pre> 355 * 356 * @return the next pseudorandom, uniformly distributed 357 * {@code boolean} value from this random number generator's 358 * sequence 359 * @since 1.2 360 */ 361 public boolean nextBoolean() { 362 return next(1) != 0; 363 } 364 365 /** 366 * Returns the next pseudorandom, uniformly distributed {@code float} 367 * value between {@code 0.0} and {@code 1.0} from this random 368 * number generator's sequence. 369 * 370 * <p>The general contract of {@code nextFloat} is that one 371 * {@code float} value, chosen (approximately) uniformly from the 372 * range {@code 0.0f} (inclusive) to {@code 1.0f} (exclusive), is 373 * pseudorandomly generated and returned. All 2<font 374 * size="-1"><sup>24</sup></font> possible {@code float} values 375 * of the form <i>m x </i>2<font 376 * size="-1"><sup>-24</sup></font>, where <i>m</i> is a positive 377 * integer less than 2<font size="-1"><sup>24</sup> </font>, are 378 * produced with (approximately) equal probability. 379 * 380 * <p>The method {@code nextFloat} is implemented by class {@code Random} 381 * as if by: 382 * <pre> {@code 383 * public float nextFloat() { 384 * return next(24) / ((float)(1 << 24)); 385 * }}</pre> 386 * 387 * <p>The hedge "approximately" is used in the foregoing description only 388 * because the next method is only approximately an unbiased source of 389 * independently chosen bits. If it were a perfect source of randomly 390 * chosen bits, then the algorithm shown would choose {@code float} 391 * values from the stated range with perfect uniformity.<p> 392 * [In early versions of Java, the result was incorrectly calculated as: 393 * <pre> {@code 394 * return next(30) / ((float)(1 << 30));}</pre> 395 * This might seem to be equivalent, if not better, but in fact it 396 * introduced a slight nonuniformity because of the bias in the rounding 397 * of floating-point numbers: it was slightly more likely that the 398 * low-order bit of the significand would be 0 than that it would be 1.] 399 * 400 * @return the next pseudorandom, uniformly distributed {@code float} 401 * value between {@code 0.0} and {@code 1.0} from this 402 * random number generator's sequence 403 */ 404 public float nextFloat() { 405 return next(24) / ((float)(1 << 24)); 406 } 407 408 /** 409 * Returns the next pseudorandom, uniformly distributed 410 * {@code double} value between {@code 0.0} and 411 * {@code 1.0} from this random number generator's sequence. 412 * 413 * <p>The general contract of {@code nextDouble} is that one 414 * {@code double} value, chosen (approximately) uniformly from the 415 * range {@code 0.0d} (inclusive) to {@code 1.0d} (exclusive), is 416 * pseudorandomly generated and returned. 417 * 418 * <p>The method {@code nextDouble} is implemented by class {@code Random} 419 * as if by: 420 * <pre> {@code 421 * public double nextDouble() { 422 * return (((long)next(26) << 27) + next(27)) 423 * / (double)(1L << 53); 424 * }}</pre> 425 * 426 * <p>The hedge "approximately" is used in the foregoing description only 427 * because the {@code next} method is only approximately an unbiased 428 * source of independently chosen bits. If it were a perfect source of 429 * randomly chosen bits, then the algorithm shown would choose 430 * {@code double} values from the stated range with perfect uniformity. 431 * <p>[In early versions of Java, the result was incorrectly calculated as: 432 * <pre> {@code 433 * return (((long)next(27) << 27) + next(27)) 434 * / (double)(1L << 54);}</pre> 435 * This might seem to be equivalent, if not better, but in fact it 436 * introduced a large nonuniformity because of the bias in the rounding 437 * of floating-point numbers: it was three times as likely that the 438 * low-order bit of the significand would be 0 than that it would be 1! 439 * This nonuniformity probably doesn't matter much in practice, but we 440 * strive for perfection.] 441 * 442 * @return the next pseudorandom, uniformly distributed {@code double} 443 * value between {@code 0.0} and {@code 1.0} from this 444 * random number generator's sequence 445 * @see Math#random 446 */ 447 public double nextDouble() { 448 return (((long)(next(26)) << 27) + next(27)) 449 / (double)(1L << 53); 450 } 451 452 private double nextNextGaussian; 453 private boolean haveNextNextGaussian = false; 454 455 /** 456 * Returns the next pseudorandom, Gaussian ("normally") distributed 457 * {@code double} value with mean {@code 0.0} and standard 458 * deviation {@code 1.0} from this random number generator's sequence. 459 * <p> 460 * The general contract of {@code nextGaussian} is that one 461 * {@code double} value, chosen from (approximately) the usual 462 * normal distribution with mean {@code 0.0} and standard deviation 463 * {@code 1.0}, is pseudorandomly generated and returned. 464 * 465 * <p>The method {@code nextGaussian} is implemented by class 466 * {@code Random} as if by a threadsafe version of the following: 467 * <pre> {@code 468 * private double nextNextGaussian; 469 * private boolean haveNextNextGaussian = false; 470 * 471 * public double nextGaussian() { 472 * if (haveNextNextGaussian) { 473 * haveNextNextGaussian = false; 474 * return nextNextGaussian; 475 * } else { 476 * double v1, v2, s; 477 * do { 478 * v1 = 2 * nextDouble() - 1; // between -1.0 and 1.0 479 * v2 = 2 * nextDouble() - 1; // between -1.0 and 1.0 480 * s = v1 * v1 + v2 * v2; 481 * } while (s >= 1 || s == 0); 482 * double multiplier = StrictMath.sqrt(-2 * StrictMath.log(s)/s); 483 * nextNextGaussian = v2 * multiplier; 484 * haveNextNextGaussian = true; 485 * return v1 * multiplier; 486 * } 487 * }}</pre> 488 * This uses the <i>polar method</i> of G. E. P. Box, M. E. Muller, and 489 * G. Marsaglia, as described by Donald E. Knuth in <i>The Art of 490 * Computer Programming</i>, Volume 3: <i>Seminumerical Algorithms</i>, 491 * section 3.4.1, subsection C, algorithm P. Note that it generates two 492 * independent values at the cost of only one call to {@code StrictMath.log} 493 * and one call to {@code StrictMath.sqrt}. 494 * 495 * @return the next pseudorandom, Gaussian ("normally") distributed 496 * {@code double} value with mean {@code 0.0} and 497 * standard deviation {@code 1.0} from this random number 498 * generator's sequence 499 */ 500 synchronized public double nextGaussian() { 501 // See Knuth, ACP, Section 3.4.1 Algorithm C. 502 if (haveNextNextGaussian) { 503 haveNextNextGaussian = false; 504 return nextNextGaussian; 505 } else { 506 double v1, v2, s; 507 do { 508 v1 = 2 * nextDouble() - 1; // between -1 and 1 509 v2 = 2 * nextDouble() - 1; // between -1 and 1 510 s = v1 * v1 + v2 * v2; 511 } while (s >= 1 || s == 0); 512 double multiplier = StrictMath.sqrt(-2 * StrictMath.log(s)/s); 513 nextNextGaussian = v2 * multiplier; 514 haveNextNextGaussian = true; 515 return v1 * multiplier; 516 } 517 } 518 519 /** 520 * Returns a stream of pseudorandom, uniformly distributed 521 * {@code integer} values from this random number generator's 522 * sequence. Values are obtained as needed by calling 523 * {@link #nextInt()}. 524 * 525 * @return an infinite stream of {@code integer} values 526 * @since 1.8 527 */ 528 public IntStream ints() { 529 return IntStream.generate(this::nextInt); 530 } 531 532 /** 533 * Returns a stream of pseudorandom, uniformly distributed 534 * {@code long} values from this random number generator's 535 * sequence. Values are obtained as needed by calling 536 * {@link #nextLong()}. 537 * 538 * @return an infinite stream of {@code long} values 539 * @since 1.8 540 */ 541 public LongStream longs() { 542 return LongStream.generate(this::nextLong); 543 } 544 545 /** 546 * Returns a stream of pseudorandom, uniformly distributed 547 * {@code double} values between {@code 0.0} and {@code 1.0} 548 * from this random number generator's sequence. Values are 549 * obtained as needed by calling {@link #nextDouble()}. 550 * 551 * @return an infinite stream of {@code double} values 552 * @since 1.8 553 */ 554 public DoubleStream doubles() { 555 return DoubleStream.generate(this::nextDouble); 556 } 557 558 /** 559 * Returns a stream of pseudorandom, Gaussian ("normally") 560 * distributed {@code double} values with mean {@code 0.0} 561 * and standard deviation {@code 1.0} from this random number 562 * generator's sequence. Values are obtained as needed by 563 * calling {@link #nextGaussian()}. 564 * 565 * @return an infinite stream of {@code double} values 566 * @since 1.8 567 */ 568 public DoubleStream gaussians() { 569 return DoubleStream.generate(this::nextGaussian); 570 } 571 572 /** 573 * Serializable fields for Random. 574 * 575 * @serialField seed long 576 * seed for random computations 577 * @serialField nextNextGaussian double 578 * next Gaussian to be returned 579 * @serialField haveNextNextGaussian boolean 580 * nextNextGaussian is valid 581 */ 582 private static final ObjectStreamField[] serialPersistentFields = { 583 new ObjectStreamField("seed", Long.TYPE), 584 new ObjectStreamField("nextNextGaussian", Double.TYPE), 585 new ObjectStreamField("haveNextNextGaussian", Boolean.TYPE) 586 }; 587 588 /** 589 * Reconstitute the {@code Random} instance from a stream (that is, 590 * deserialize it). 591 */ 592 private void readObject(java.io.ObjectInputStream s) 593 throws java.io.IOException, ClassNotFoundException { 594 595 ObjectInputStream.GetField fields = s.readFields(); 596 597 // The seed is read in as {@code long} for 598 // historical reasons, but it is converted to an AtomicLong. 599 long seedVal = fields.get("seed", -1L); 600 if (seedVal < 0) 601 throw new java.io.StreamCorruptedException( 602 "Random: invalid seed"); 603 resetSeed(seedVal); 604 nextNextGaussian = fields.get("nextNextGaussian", 0.0); 605 haveNextNextGaussian = fields.get("haveNextNextGaussian", false); 606 } 607 608 /** 609 * Save the {@code Random} instance to a stream. 610 */ 611 synchronized private void writeObject(ObjectOutputStream s) 612 throws IOException { 613 614 // set the values of the Serializable fields 615 ObjectOutputStream.PutField fields = s.putFields(); 616 617 // The seed is serialized as a long for historical reasons. 618 fields.put("seed", seed.get()); 619 fields.put("nextNextGaussian", nextNextGaussian); 620 fields.put("haveNextNextGaussian", haveNextNextGaussian); 621 622 // save them 623 s.writeFields(); 624 } 625 626 // Support for resetting seed while deserializing 627 private static final Unsafe unsafe = Unsafe.getUnsafe(); 628 private static final long seedOffset; 629 static { 630 try { 631 seedOffset = unsafe.objectFieldOffset 632 (Random.class.getDeclaredField("seed")); 633 } catch (Exception ex) { throw new Error(ex); } 634 } 635 private void resetSeed(long seedVal) { 636 unsafe.putObjectVolatile(this, seedOffset, new AtomicLong(seedVal)); 637 } 638 }