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<sup>32</sup> possible 229 * {@code int} values are produced with (approximately) equal probability. 230 * 231 * <p>The method {@code nextInt} is implemented by class {@code Random} 232 * as if by: 233 * <pre> {@code 234 * public int nextInt() { 235 * return next(32); 236 * }}</pre> 237 * 238 * @return the next pseudorandom, uniformly distributed {@code int} 239 * value from this random number generator's sequence 240 */ 241 public int nextInt() { 242 return next(32); 243 } 244 245 /** 246 * Returns a pseudorandom, uniformly distributed {@code int} value 247 * between 0 (inclusive) and the specified value (exclusive), drawn from 248 * this random number generator's sequence. The general contract of 249 * {@code nextInt} is that one {@code int} value in the specified range 250 * is pseudorandomly generated and returned. All {@code n} possible 251 * {@code int} values are produced with (approximately) equal 252 * probability. The method {@code nextInt(int n)} is implemented by 253 * class {@code Random} as if by: 254 * <pre> {@code 255 * public int nextInt(int n) { 256 * if (n <= 0) 257 * throw new IllegalArgumentException("n must be positive"); 258 * 259 * if ((n & -n) == n) // i.e., n is a power of 2 260 * return (int)((n * (long)next(31)) >> 31); 261 * 262 * int bits, val; 263 * do { 264 * bits = next(31); 265 * val = bits % n; 266 * } while (bits - val + (n-1) < 0); 267 * return val; 268 * }}</pre> 269 * 270 * <p>The hedge "approximately" is used in the foregoing description only 271 * because the next method is only approximately an unbiased source of 272 * independently chosen bits. If it were a perfect source of randomly 273 * chosen bits, then the algorithm shown would choose {@code int} 274 * values from the stated range with perfect uniformity. 275 * <p> 276 * The algorithm is slightly tricky. It rejects values that would result 277 * in an uneven distribution (due to the fact that 2^31 is not divisible 278 * by n). The probability of a value being rejected depends on n. The 279 * worst case is n=2^30+1, for which the probability of a reject is 1/2, 280 * and the expected number of iterations before the loop terminates is 2. 281 * <p> 282 * The algorithm treats the case where n is a power of two specially: it 283 * returns the correct number of high-order bits from the underlying 284 * pseudo-random number generator. In the absence of special treatment, 285 * the correct number of <i>low-order</i> bits would be returned. Linear 286 * congruential pseudo-random number generators such as the one 287 * implemented by this class are known to have short periods in the 288 * sequence of values of their low-order bits. Thus, this special case 289 * greatly increases the length of the sequence of values returned by 290 * successive calls to this method if n is a small power of two. 291 * 292 * @param n the bound on the random number to be returned. Must be 293 * positive. 294 * @return the next pseudorandom, uniformly distributed {@code int} 295 * value between {@code 0} (inclusive) and {@code n} (exclusive) 296 * from this random number generator's sequence 297 * @throws IllegalArgumentException if n is not positive 298 * @since 1.2 299 */ 300 301 public int nextInt(int n) { 302 if (n <= 0) 303 throw new IllegalArgumentException("n must be positive"); 304 305 if ((n & -n) == n) // i.e., n is a power of 2 306 return (int)((n * (long)next(31)) >> 31); 307 308 int bits, val; 309 do { 310 bits = next(31); 311 val = bits % n; 312 } while (bits - val + (n-1) < 0); 313 return val; 314 } 315 316 /** 317 * Returns the next pseudorandom, uniformly distributed {@code long} 318 * value from this random number generator's sequence. The general 319 * contract of {@code nextLong} is that one {@code long} value is 320 * pseudorandomly generated and returned. 321 * 322 * <p>The method {@code nextLong} is implemented by class {@code Random} 323 * as if by: 324 * <pre> {@code 325 * public long nextLong() { 326 * return ((long)next(32) << 32) + next(32); 327 * }}</pre> 328 * 329 * Because class {@code Random} uses a seed with only 48 bits, 330 * this algorithm will not return all possible {@code long} values. 331 * 332 * @return the next pseudorandom, uniformly distributed {@code long} 333 * value from this random number generator's sequence 334 */ 335 public long nextLong() { 336 // it's okay that the bottom word remains signed. 337 return ((long)(next(32)) << 32) + next(32); 338 } 339 340 /** 341 * Returns the next pseudorandom, uniformly distributed 342 * {@code boolean} value from this random number generator's 343 * sequence. The general contract of {@code nextBoolean} is that one 344 * {@code boolean} value is pseudorandomly generated and returned. The 345 * values {@code true} and {@code false} are produced with 346 * (approximately) equal probability. 347 * 348 * <p>The method {@code nextBoolean} is implemented by class {@code Random} 349 * as if by: 350 * <pre> {@code 351 * public boolean nextBoolean() { 352 * return next(1) != 0; 353 * }}</pre> 354 * 355 * @return the next pseudorandom, uniformly distributed 356 * {@code boolean} value from this random number generator's 357 * sequence 358 * @since 1.2 359 */ 360 public boolean nextBoolean() { 361 return next(1) != 0; 362 } 363 364 /** 365 * Returns the next pseudorandom, uniformly distributed {@code float} 366 * value between {@code 0.0} and {@code 1.0} from this random 367 * number generator's sequence. 368 * 369 * <p>The general contract of {@code nextFloat} is that one 370 * {@code float} value, chosen (approximately) uniformly from the 371 * range {@code 0.0f} (inclusive) to {@code 1.0f} (exclusive), is 372 * pseudorandomly generated and returned. All 2<sup>24</sup> possible 373 * {@code float} values of the form <i>m x </i>2<sup>-24</sup>, 374 * where <i>m</i> is a positive integer less than 2<sup>24</sup>, are 375 * produced with (approximately) equal probability. 376 * 377 * <p>The method {@code nextFloat} is implemented by class {@code Random} 378 * as if by: 379 * <pre> {@code 380 * public float nextFloat() { 381 * return next(24) / ((float)(1 << 24)); 382 * }}</pre> 383 * 384 * <p>The hedge "approximately" is used in the foregoing description only 385 * because the next method is only approximately an unbiased source of 386 * independently chosen bits. If it were a perfect source of randomly 387 * chosen bits, then the algorithm shown would choose {@code float} 388 * values from the stated range with perfect uniformity.<p> 389 * [In early versions of Java, the result was incorrectly calculated as: 390 * <pre> {@code 391 * return next(30) / ((float)(1 << 30));}</pre> 392 * This might seem to be equivalent, if not better, but in fact it 393 * introduced a slight nonuniformity because of the bias in the rounding 394 * of floating-point numbers: it was slightly more likely that the 395 * low-order bit of the significand would be 0 than that it would be 1.] 396 * 397 * @return the next pseudorandom, uniformly distributed {@code float} 398 * value between {@code 0.0} and {@code 1.0} from this 399 * random number generator's sequence 400 */ 401 public float nextFloat() { 402 return next(24) / ((float)(1 << 24)); 403 } 404 405 /** 406 * Returns the next pseudorandom, uniformly distributed 407 * {@code double} value between {@code 0.0} and 408 * {@code 1.0} from this random number generator's sequence. 409 * 410 * <p>The general contract of {@code nextDouble} is that one 411 * {@code double} value, chosen (approximately) uniformly from the 412 * range {@code 0.0d} (inclusive) to {@code 1.0d} (exclusive), is 413 * pseudorandomly generated and returned. 414 * 415 * <p>The method {@code nextDouble} is implemented by class {@code Random} 416 * as if by: 417 * <pre> {@code 418 * public double nextDouble() { 419 * return (((long)next(26) << 27) + next(27)) 420 * / (double)(1L << 53); 421 * }}</pre> 422 * 423 * <p>The hedge "approximately" is used in the foregoing description only 424 * because the {@code next} method is only approximately an unbiased 425 * source of independently chosen bits. If it were a perfect source of 426 * randomly chosen bits, then the algorithm shown would choose 427 * {@code double} values from the stated range with perfect uniformity. 428 * <p>[In early versions of Java, the result was incorrectly calculated as: 429 * <pre> {@code 430 * return (((long)next(27) << 27) + next(27)) 431 * / (double)(1L << 54);}</pre> 432 * This might seem to be equivalent, if not better, but in fact it 433 * introduced a large nonuniformity because of the bias in the rounding 434 * of floating-point numbers: it was three times as likely that the 435 * low-order bit of the significand would be 0 than that it would be 1! 436 * This nonuniformity probably doesn't matter much in practice, but we 437 * strive for perfection.] 438 * 439 * @return the next pseudorandom, uniformly distributed {@code double} 440 * value between {@code 0.0} and {@code 1.0} from this 441 * random number generator's sequence 442 * @see Math#random 443 */ 444 public double nextDouble() { 445 return (((long)(next(26)) << 27) + next(27)) 446 / (double)(1L << 53); 447 } 448 449 private double nextNextGaussian; 450 private boolean haveNextNextGaussian = false; 451 452 /** 453 * Returns the next pseudorandom, Gaussian ("normally") distributed 454 * {@code double} value with mean {@code 0.0} and standard 455 * deviation {@code 1.0} from this random number generator's sequence. 456 * <p> 457 * The general contract of {@code nextGaussian} is that one 458 * {@code double} value, chosen from (approximately) the usual 459 * normal distribution with mean {@code 0.0} and standard deviation 460 * {@code 1.0}, is pseudorandomly generated and returned. 461 * 462 * <p>The method {@code nextGaussian} is implemented by class 463 * {@code Random} as if by a threadsafe version of the following: 464 * <pre> {@code 465 * private double nextNextGaussian; 466 * private boolean haveNextNextGaussian = false; 467 * 468 * public double nextGaussian() { 469 * if (haveNextNextGaussian) { 470 * haveNextNextGaussian = false; 471 * return nextNextGaussian; 472 * } else { 473 * double v1, v2, s; 474 * do { 475 * v1 = 2 * nextDouble() - 1; // between -1.0 and 1.0 476 * v2 = 2 * nextDouble() - 1; // between -1.0 and 1.0 477 * s = v1 * v1 + v2 * v2; 478 * } while (s >= 1 || s == 0); 479 * double multiplier = StrictMath.sqrt(-2 * StrictMath.log(s)/s); 480 * nextNextGaussian = v2 * multiplier; 481 * haveNextNextGaussian = true; 482 * return v1 * multiplier; 483 * } 484 * }}</pre> 485 * This uses the <i>polar method</i> of G. E. P. Box, M. E. Muller, and 486 * G. Marsaglia, as described by Donald E. Knuth in <i>The Art of 487 * Computer Programming</i>, Volume 3: <i>Seminumerical Algorithms</i>, 488 * section 3.4.1, subsection C, algorithm P. Note that it generates two 489 * independent values at the cost of only one call to {@code StrictMath.log} 490 * and one call to {@code StrictMath.sqrt}. 491 * 492 * @return the next pseudorandom, Gaussian ("normally") distributed 493 * {@code double} value with mean {@code 0.0} and 494 * standard deviation {@code 1.0} from this random number 495 * generator's sequence 496 */ 497 synchronized public double nextGaussian() { 498 // See Knuth, ACP, Section 3.4.1 Algorithm C. 499 if (haveNextNextGaussian) { 500 haveNextNextGaussian = false; 501 return nextNextGaussian; 502 } else { 503 double v1, v2, s; 504 do { 505 v1 = 2 * nextDouble() - 1; // between -1 and 1 506 v2 = 2 * nextDouble() - 1; // between -1 and 1 507 s = v1 * v1 + v2 * v2; 508 } while (s >= 1 || s == 0); 509 double multiplier = StrictMath.sqrt(-2 * StrictMath.log(s)/s); 510 nextNextGaussian = v2 * multiplier; 511 haveNextNextGaussian = true; 512 return v1 * multiplier; 513 } 514 } 515 516 /** 517 * Returns a stream of pseudorandom, uniformly distributed 518 * {@code integer} values from this random number generator's 519 * sequence. Values are obtained as needed by calling 520 * {@link #nextInt()}. 521 * 522 * @return an infinite stream of {@code integer} values 523 * @since 1.8 524 */ 525 public IntStream ints() { 526 return IntStream.generate(this::nextInt); 527 } 528 529 /** 530 * Returns a stream of pseudorandom, uniformly distributed 531 * {@code long} values from this random number generator's 532 * sequence. Values are obtained as needed by calling 533 * {@link #nextLong()}. 534 * 535 * @return an infinite stream of {@code long} values 536 * @since 1.8 537 */ 538 public LongStream longs() { 539 return LongStream.generate(this::nextLong); 540 } 541 542 /** 543 * Returns a stream of pseudorandom, uniformly distributed 544 * {@code double} values between {@code 0.0} and {@code 1.0} 545 * from this random number generator's sequence. Values are 546 * obtained as needed by calling {@link #nextDouble()}. 547 * 548 * @return an infinite stream of {@code double} values 549 * @since 1.8 550 */ 551 public DoubleStream doubles() { 552 return DoubleStream.generate(this::nextDouble); 553 } 554 555 /** 556 * Returns a stream of pseudorandom, Gaussian ("normally") 557 * distributed {@code double} values with mean {@code 0.0} 558 * and standard deviation {@code 1.0} from this random number 559 * generator's sequence. Values are obtained as needed by 560 * calling {@link #nextGaussian()}. 561 * 562 * @return an infinite stream of {@code double} values 563 * @since 1.8 564 */ 565 public DoubleStream gaussians() { 566 return DoubleStream.generate(this::nextGaussian); 567 } 568 569 /** 570 * Serializable fields for Random. 571 * 572 * @serialField seed long 573 * seed for random computations 574 * @serialField nextNextGaussian double 575 * next Gaussian to be returned 576 * @serialField haveNextNextGaussian boolean 577 * nextNextGaussian is valid 578 */ 579 private static final ObjectStreamField[] serialPersistentFields = { 580 new ObjectStreamField("seed", Long.TYPE), 581 new ObjectStreamField("nextNextGaussian", Double.TYPE), 582 new ObjectStreamField("haveNextNextGaussian", Boolean.TYPE) 583 }; 584 585 /** 586 * Reconstitute the {@code Random} instance from a stream (that is, 587 * deserialize it). 588 */ 589 private void readObject(java.io.ObjectInputStream s) 590 throws java.io.IOException, ClassNotFoundException { 591 592 ObjectInputStream.GetField fields = s.readFields(); 593 594 // The seed is read in as {@code long} for 595 // historical reasons, but it is converted to an AtomicLong. 596 long seedVal = fields.get("seed", -1L); 597 if (seedVal < 0) 598 throw new java.io.StreamCorruptedException( 599 "Random: invalid seed"); 600 resetSeed(seedVal); 601 nextNextGaussian = fields.get("nextNextGaussian", 0.0); 602 haveNextNextGaussian = fields.get("haveNextNextGaussian", false); 603 } 604 605 /** 606 * Save the {@code Random} instance to a stream. 607 */ 608 synchronized private void writeObject(ObjectOutputStream s) 609 throws IOException { 610 611 // set the values of the Serializable fields 612 ObjectOutputStream.PutField fields = s.putFields(); 613 614 // The seed is serialized as a long for historical reasons. 615 fields.put("seed", seed.get()); 616 fields.put("nextNextGaussian", nextNextGaussian); 617 fields.put("haveNextNextGaussian", haveNextNextGaussian); 618 619 // save them 620 s.writeFields(); 621 } 622 623 // Support for resetting seed while deserializing 624 private static final Unsafe unsafe = Unsafe.getUnsafe(); 625 private static final long seedOffset; 626 static { 627 try { 628 seedOffset = unsafe.objectFieldOffset 629 (Random.class.getDeclaredField("seed")); 630 } catch (Exception ex) { throw new Error(ex); } 631 } 632 private void resetSeed(long seedVal) { 633 unsafe.putObjectVolatile(this, seedOffset, new AtomicLong(seedVal)); 634 } 635 }