Doc: Proper attribution (JuliaLang#41205)

* Doc: Proper attribution

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* Update Xoshiro.jl

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* Fix whitespace

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src/task.c

@@ -649,14 +649,16 @@ JL_DLLEXPORT void jl_rethrow_other(jl_value_t *e JL_MAYBE_UNROOTED)
  throw_internal(ct, NULL);
 }
 
-/* This is xoshiro256++ 1.0, used for tasklocal random number generation in julia.
+/* This is xoshiro256++ 1.0, used for tasklocal random number generation in Julia.
  This implementation is intended for embedders and internal use by the runtime, and is
- based on the reference implementation on http:https://prng.di.unimi.it
+ based on the reference implementation at https:https://prng.di.unimi.it
 
- Credits go to Sebastiano Vigna for coming up with this PRNG.
+ Credits go to David Blackman and Sebastiano Vigna for coming up with this PRNG.
+ They described xoshiro256++ in "Scrambled Linear Pseudorandom Number Generators",
+ ACM Trans. Math. Softw., 2021.
 
- There is a pure julia implementation in stdlib that tends to be faster when used from
- within julia, due to inlining and more agressive architecture-specific optimizations.
+ There is a pure Julia implementation in stdlib that tends to be faster when used from
+ within Julia, due to inlining and more agressive architecture-specific optimizations.
 */
 JL_DLLEXPORT uint64_t jl_tasklocal_genrandom(jl_task_t *task) JL_NOTSAFEPOINT
 {

stdlib/Random/src/Xoshiro.jl

@@ -6,8 +6,10 @@
 """
  Xoshiro
 
-Xoshiro256++ is a fast pseudorandom number generator originally developed by Sebastian Vigna.
-Reference implementation is available at https://prng.di.unimi.it
+Xoshiro256++ is a fast pseudorandom number generator described by David Blackman and
+Sebastiano Vigna in "Scrambled Linear Pseudorandom Number Generators",
+ACM Trans. Math. Softw., 2021. Reference implementation is available
+at https://prng.di.unimi.it
 
 Apart from the high speed, Xoshiro has a small memory footprint, making it suitable for
 applications where many different random states need to be held for long time.