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8274685: Documentation suggests there are ArbitrarilyJumpableGenerator when none

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Co-authored-by: Guy Steele <gls@openjdk.org>
Reviewed-by: rriggs
This commit is contained in:
Jim Laskey 2021-11-22 16:19:23 +00:00
parent 6b4fbaedbb
commit 8683de5eda
1 changed files with 16 additions and 11 deletions
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src/java.base/share/classes/java/util/random/package-info.java
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@ -347,12 +347,12 @@
* supported by the interface {@link RandomGenerator.JumpableGenerator}.
* Sometimes it is desirable to support two levels of jumping (by long distances
* and by <i>really</i> long distances); this strategy is supported by the
* interface {@link RandomGenerator.LeapableGenerator}. There is also an interface
* interface {@link RandomGenerator.LeapableGenerator}. In this package,
* implementations of this interface include "Xoroshiro128PlusPlus" and
* "Xoshiro256PlusPlus". There is also an interface
* {@link 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".
* jumping along the state cycle by any user-specified distance; there are currently
* no implementations of this interface in this package.
*
* <p> A more recent category of "splittable" pseudorandom generator algorithms
* uses a large family of state cycles and makes some attempt to ensure that
@ -382,13 +382,18 @@
* 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
* Vigna in "Scrambled Linear Pseudorandom Number Generators", <i>ACM Transactions
* on Mathematical Software</i>, 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 {@code SplittableRandom}.
* in 2019 by Steele and Vigna, described in "Computationally Easy, Spectrally
* Good Multipliers for Congruential Pseudorandom Number Generators",
* <i>Software: Practice and Experience</i> (2021), doi:10.1002/spe.3030),
* and then applies either a mixing function identified by Doug Lea or
* 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 {@code SplittableRandom}
* (see Steele and Vigna, "LXM: Better Splittable Pseudorandom Number
* Generators (and Almost as Fast)", <i>Proc. 2021 ACM OOPSLA Conference</i>).
*
* Each class with a name of the form
* {@code L}<i>p</i>{@code X}<i>q</i>{@code SomethingRandom}