rand

Forked version because cannot import:

github.com/flyingmutant/rand: github.com/flyingmutant/rand@v0.2.7: parsing go.mod:
module declares its path as: pgregory.net/rand
        but was required as: github.com/flyingmutant/rand

pregory.net/rand: cannot find module providing package pregory.net/rand: unrecognized import path "pregory.net/rand": https fetch: Get "https://pregory.net/rand?go-get=1": dial tcp: lookup pregory.net: no such host

This one uses import "github.com/kokizzu/rand", contact original creator if you have issues or patch

Fast, high quality alternative to math/rand and golang.org/x/exp/rand.

Compared to these packages, github.com/kokizzu/rand:

• is API-compatible with all *rand.Rand methods,
• is significantly faster, while improving the generator quality,
• has simpler generator initialization:
  • rand.New() instead of rand.New(rand.NewSource(time.Now().UnixNano()))
  • rand.New(1) instead of rand.New(rand.NewSource(1))
• is deliberately not providing most top-level functions like ExpFloat64() or Int() and the Source interface.

Benchmarks

All benchmarks were run on Intel(R) Xeon(R) CPU E5-2680 v3 @ 2.50GHz, linux/amd64.

Compared to math/rand:

name                     old time/op    new time/op    delta
Float64-48                  180ns ± 8%       1ns ±12%   -99.66%  (p=0.000 n=10+9)
Intn-48                     186ns ± 2%       1ns ±11%   -99.63%  (p=0.000 n=10+10)
Intn_Big-48                 200ns ± 1%       1ns ±19%   -99.28%  (p=0.000 n=8+10)
Uint64-48                   193ns ± 5%       1ns ± 3%   -99.72%  (p=0.000 n=9+9)
Rand_New                   12.7µs ± 4%     0.1µs ± 6%   -99.38%  (p=0.000 n=10+10)
Rand_ExpFloat64            9.66ns ± 3%    5.90ns ± 3%   -38.97%  (p=0.000 n=10+10)
Rand_Float32               8.90ns ± 5%    2.04ns ± 4%   -77.05%  (p=0.000 n=10+10)
Rand_Float64               7.62ns ± 4%    2.93ns ± 3%   -61.50%  (p=0.000 n=10+10)
Rand_Int                   7.74ns ± 3%    2.92ns ± 2%   -62.30%  (p=0.000 n=10+10)
Rand_Int31                 7.81ns ± 3%    1.92ns ±14%   -75.39%  (p=0.000 n=10+10)
Rand_Int31n                12.7ns ± 3%     3.0ns ± 3%   -76.66%  (p=0.000 n=9+10)
Rand_Int31n_Big            12.6ns ± 4%     3.4ns ±10%   -73.39%  (p=0.000 n=10+10)
Rand_Int63                 7.78ns ± 4%    2.96ns ± 6%   -61.97%  (p=0.000 n=10+9)
Rand_Int63n                26.4ns ± 1%     3.6ns ± 3%   -86.56%  (p=0.000 n=10+10)
Rand_Int63n_Big            26.2ns ± 7%     6.2ns ± 4%   -76.53%  (p=0.000 n=10+10)
Rand_Intn                  14.4ns ± 5%     3.5ns ± 3%   -75.72%  (p=0.000 n=10+10)
Rand_Intn_Big              28.8ns ± 2%     6.0ns ± 6%   -79.03%  (p=0.000 n=10+10)
Rand_NormFloat64           10.7ns ± 6%     6.0ns ± 3%   -44.28%  (p=0.000 n=10+10)
Rand_Perm                  1.34µs ± 1%    0.39µs ± 3%   -70.86%  (p=0.000 n=9+10)
Rand_Read                   289ns ± 5%     104ns ± 5%   -64.00%  (p=0.000 n=10+10)
Rand_Seed                  10.9µs ± 4%     0.0µs ± 6%   -99.69%  (p=0.000 n=10+10)
Rand_Shuffle                790ns ± 4%     374ns ± 5%   -52.63%  (p=0.000 n=10+10)
Rand_ShuffleOverhead        522ns ± 3%     204ns ± 4%   -60.83%  (p=0.000 n=10+10)
Rand_Uint32                7.82ns ± 3%    1.72ns ± 3%   -77.98%  (p=0.000 n=10+9)
Rand_Uint64                9.59ns ± 3%    2.83ns ± 2%   -70.47%  (p=0.000 n=10+9)

name                     old alloc/op   new alloc/op   delta
Rand_New                   5.42kB ± 0%    0.05kB ± 0%   -99.12%  (p=0.000 n=10+10)
Rand_Perm                    416B ± 0%      416B ± 0%      ~     (all equal)

name                     old allocs/op  new allocs/op  delta
Rand_New                     2.00 ± 0%      1.00 ± 0%   -50.00%  (p=0.000 n=10+10)
Rand_Perm                    1.00 ± 0%      1.00 ± 0%      ~     (all equal)

name                     old speed      new speed      delta
Rand_Read                 887MB/s ± 4%  2464MB/s ± 4%  +177.83%  (p=0.000 n=10+10)
Rand_Uint32               511MB/s ± 3%  2306MB/s ± 7%  +350.86%  (p=0.000 n=10+10)
Rand_Uint64               834MB/s ± 3%  2811MB/s ± 4%  +236.85%  (p=0.000 n=10+10)

Compared to golang.org/x/exp/rand:

name                     old time/op    new time/op    delta
Float64-48                  175ns ± 8%       1ns ±12%   -99.65%  (p=0.000 n=10+9)
Intn-48                     176ns ±10%       1ns ±11%   -99.61%  (p=0.000 n=10+10)
Intn_Big-48                 174ns ± 1%       1ns ±19%   -99.18%  (p=0.000 n=9+10)
Uint64-48                   157ns ± 5%       1ns ± 3%   -99.66%  (p=0.000 n=10+9)
Rand_New                   78.8ns ± 6%    78.3ns ± 6%      ~     (p=0.853 n=10+10)
Rand_ExpFloat64            8.94ns ± 6%    5.90ns ± 3%   -34.00%  (p=0.000 n=10+10)
Rand_Float32               9.67ns ± 5%    2.04ns ± 4%   -78.89%  (p=0.000 n=10+10)
Rand_Float64               8.56ns ± 5%    2.93ns ± 3%   -65.74%  (p=0.000 n=10+10)
Rand_Int                   5.75ns ± 3%    2.92ns ± 2%   -49.25%  (p=0.000 n=9+10)
Rand_Int31                 5.72ns ± 5%    1.92ns ±14%   -66.37%  (p=0.000 n=10+10)
Rand_Int31n                17.4ns ± 7%     3.0ns ± 3%   -82.87%  (p=0.000 n=10+10)
Rand_Int31n_Big            17.3ns ± 4%     3.4ns ±10%   -80.57%  (p=0.000 n=10+10)
Rand_Int63                 5.77ns ± 4%    2.96ns ± 6%   -48.73%  (p=0.000 n=10+9)
Rand_Int63n                17.0ns ± 2%     3.6ns ± 3%   -79.13%  (p=0.000 n=9+10)
Rand_Int63n_Big            26.5ns ± 2%     6.2ns ± 4%   -76.81%  (p=0.000 n=10+10)
Rand_Intn                  17.5ns ± 5%     3.5ns ± 3%   -79.94%  (p=0.000 n=10+10)
Rand_Intn_Big              27.5ns ± 3%     6.0ns ± 6%   -78.09%  (p=0.000 n=10+10)
Rand_NormFloat64           10.0ns ± 3%     6.0ns ± 3%   -40.45%  (p=0.000 n=10+10)
Rand_Perm                  1.31µs ± 1%    0.39µs ± 3%   -70.04%  (p=0.000 n=10+10)
Rand_Read                   334ns ± 1%     104ns ± 5%   -68.88%  (p=0.000 n=8+10)
Rand_Seed                  5.36ns ± 2%   33.73ns ± 6%  +528.91%  (p=0.000 n=10+10)
Rand_Shuffle               1.22µs ± 2%    0.37µs ± 5%   -69.36%  (p=0.000 n=10+10)
Rand_ShuffleOverhead        907ns ± 2%     204ns ± 4%   -77.45%  (p=0.000 n=10+10)
Rand_Uint32                5.20ns ± 5%    1.72ns ± 3%   -66.84%  (p=0.000 n=10+9)
Rand_Uint64                5.14ns ± 5%    2.83ns ± 2%   -44.85%  (p=0.000 n=10+9)
Rand_Uint64n               17.6ns ± 3%     3.5ns ± 2%   -80.32%  (p=0.000 n=10+10)
Rand_Uint64n_Big           27.3ns ± 2%     6.0ns ± 7%   -77.97%  (p=0.000 n=10+10)
Rand_MarshalBinary         30.5ns ± 1%     3.8ns ± 4%   -87.70%  (p=0.000 n=8+10)
Rand_UnmarshalBinary       3.22ns ± 4%    3.71ns ± 3%   +15.16%  (p=0.000 n=10+10)

name                     old alloc/op   new alloc/op   delta
Rand_New                    48.0B ± 0%     48.0B ± 0%      ~     (all equal)
Rand_Perm                    416B ± 0%      416B ± 0%      ~     (all equal)
Rand_MarshalBinary          16.0B ± 0%      0.0B       -100.00%  (p=0.000 n=10+10)
Rand_UnmarshalBinary        0.00B          0.00B           ~     (all equal)

name                     old allocs/op  new allocs/op  delta
Rand_New                     2.00 ± 0%      1.00 ± 0%   -50.00%  (p=0.000 n=10+10)
Rand_Perm                    1.00 ± 0%      1.00 ± 0%      ~     (all equal)
Rand_MarshalBinary           1.00 ± 0%      0.00       -100.00%  (p=0.000 n=10+10)
Rand_UnmarshalBinary         0.00           0.00           ~     (all equal)

name                     old speed      new speed      delta
Rand_Read                 764MB/s ± 3%  2464MB/s ± 4%  +222.68%  (p=0.000 n=9+10)
Rand_Uint32               770MB/s ± 5%  2306MB/s ± 7%  +199.35%  (p=0.000 n=10+10)
Rand_Uint64              1.56GB/s ± 5%  2.81GB/s ± 4%   +80.32%  (p=0.000 n=10+10)

Compared to github.com/valyala/fastrand:

Note that fastrand does not generate good random numbers.

name       old time/op  new time/op  delta
Uint64-48  3.20ns ±35%  0.53ns ± 3%  -83.29%  (p=0.000 n=10+9)
Intn-48    1.83ns ±21%  0.69ns ±11%  -62.35%  (p=0.000 n=10+10)

FAQ

Why did you write this?

math/rand is both slow and not up to standards in terms of quality (but can not be changed because of Go 1 compatibility promise). golang.org/x/exp/rand fixes some (but not all) quality issues, without improving the speed, and it seems that there is no active development happening there.

How does this thing work?

This package builds on 3 primitives: raw 64-bit generation using sfc64, floating-point generation using floating-point multiplication, and integer generation in range using 32.64 or 64.128 fixed-point multiplication.

Why is it fast?

The primitives selected are (as far as I am aware) about as fast as you can go without sacrificing quality. On top of that, it is mainly making sure the compiler is able to inline code, and a couple of micro-optimizations.

Why no Source?

In Go (but not in C++ or Rust) it is a costly abstraction that provides no real value. How often do you use a non-default Source with math/rand?

Why no top-level functions?

Dislike for global mutable state. Also, without some kind of thread-local state they are very slow (because global state needs to be mutex-protected). If you like the convenience of top-level functions, math/rand is a fine choice. And if you just need a couple of random integers and don't care about the performance, rand.New().Int() works too. As an exception, rand.Uint64(), rand.Intn() and rand.Float64() are provided to ease porting of applications relying on a global random number generator that is safe for concurrent use.

Why sfc64?

I like it. It has withstood the test of time, with no known flaws or weaknesses despite a lot of effort and CPU-hours spent on finding them. Also, it provides guarantees about period length and distance between generators seeded with different seeds. And it is fast.

Why not...

...pcg?

A bit slow. Otherwise, pcg64dxsm is probably a fine choice.

...xoshiro/xoroshiro?

Quite a bit of controversy and people finding weaknesses in variants of this design. Did you know that xoshiro256**, which author describes as an "all-purpose, rock-solid generator" that "passes all tests we are aware of", fails them in seconds if you multiply the output by 57?

...splitmix?

With 64-bit state and 64-bit output, splitmix64 outputs every 64-bit number exactly once over its 2^64 period — in other words, the probability of generating the same number is 0. A birthday test will quickly find this problem.

...wyrand?

An excellent generator if you are OK with slightly lower quality. Because its output function (unlike splitmix) is not a bijection, some outputs are more likely to appear than others. You can easily observe this non-uniformity with a birthday test.

...romu?

Very fast, but relatively new and untested. Also, no guarantees about the period length.

Status

github.com/kokizzu/rand is preparing for stable 1.0 release. Deterministic pseudo-random generation produces the same results on 32-bit and 64-bit architectures, both little-endian and big-endian. After 1.0, any observable change to these results would only occur together with a major version bump.

License

github.com/kokizzu/rand is licensed under the Mozilla Public License Version 2.0.