Fast, high quality alternative to math/rand and golang.org/x/exp/rand.
Compared to these packages, pgregory.net/rand:
- is API-compatible with all
*rand.Randmethods and all top-level functions exceptSeed(), - is significantly faster, while improving the generator quality,
- has simpler generator initialization:
rand.New()instead ofrand.New(rand.NewSource(time.Now().UnixNano()))rand.New(1)instead ofrand.New(rand.NewSource(1))
- is deliberately not providing top-level
Seed()and theSourceinterface.
Compared to math/rand/v2 (top-level functions & PCG source):
Intel(R) Core(TM) i7-10510U CPU @ 1.80GHz, linux/amd64
name old time/op new time/op delta
Float64-8 3.29ns ± 2% 3.55ns ±15% +7.92% (p=0.006 n=9+10)
Intn-8 3.86ns ± 1% 3.96ns ± 4% +2.58% (p=0.002 n=9+8)
Intn_Big-8 6.46ns ± 2% 7.45ns ±15% +15.35% (p=0.000 n=8+10)
Uint64-8 3.06ns ± 1% 3.20ns ± 2% +4.75% (p=0.000 n=10+8)
Rand_New 107ns ± 1% 102ns ± 1% -4.85% (p=0.000 n=9+9)
Rand_ExpFloat64 13.0ns ± 1% 8.9ns ± 3% -31.25% (p=0.000 n=9+8)
Rand_Float32 7.67ns ± 3% 3.61ns ± 2% -52.93% (p=0.000 n=9+10)
Rand_Float64 7.95ns ± 3% 4.22ns ± 2% -46.99% (p=0.000 n=10+9)
Rand_Int 6.79ns ± 1% 3.90ns ± 0% -42.54% (p=0.000 n=10+8)
Rand_Int31 6.79ns ± 1% 2.56ns ± 1% -62.30% (p=0.000 n=10+9)
Rand_Int31n 10.2ns ± 1% 4.2ns ± 1% -59.15% (p=0.000 n=9+9)
Rand_Int31n_Big 10.2ns ± 1% 4.2ns ± 1% -59.25% (p=0.000 n=9+10)
Rand_Int63 6.90ns ± 4% 3.92ns ± 1% -43.20% (p=0.000 n=9+8)
Rand_Int63n 10.3ns ± 5% 5.5ns ± 1% -46.40% (p=0.000 n=9+8)
Rand_Int63n_Big 28.8ns ± 1% 8.8ns ± 1% -69.44% (p=0.000 n=10+8)
Rand_Intn 10.2ns ± 1% 5.5ns ± 0% -46.75% (p=0.000 n=9+8)
Rand_Intn_Big 28.7ns ± 1% 8.8ns ± 1% -69.44% (p=0.000 n=9+8)
Rand_NormFloat64 14.1ns ± 2% 9.1ns ± 1% -35.19% (p=0.000 n=9+9)
Rand_Perm 951ns ± 5% 451ns ± 1% -52.60% (p=0.000 n=9+9)
Rand_Seed 0.57ns ± 3% 46.09ns ± 1% +7995.18% (p=0.000 n=10+9)
Rand_Shuffle 711ns ± 1% 412ns ± 1% -42.12% (p=0.000 n=10+8)
Rand_ShuffleOverhead 638ns ± 3% 306ns ± 1% -52.09% (p=0.000 n=9+10)
Rand_Uint32 7.12ns ±12% 2.48ns ± 1% -65.24% (p=0.000 n=10+9)
Rand_Uint64 6.86ns ± 1% 3.93ns ± 0% -42.64% (p=0.000 n=8+9)
ShuffleSlice 284ns ± 1% 278ns ± 1% -2.09% (p=0.000 n=8+8)
name old speed new speed delta
Uint64 2.62GB/s ± 1% 2.48GB/s ± 7% -5.30% (p=0.000 n=10+9)
Rand_Uint32 564MB/s ±11% 1616MB/s ± 1% +186.63% (p=0.000 n=10+9)
Rand_Uint64 1.17GB/s ± 1% 2.03GB/s ± 0% +74.32% (p=0.000 n=8+9)
name old alloc/op new alloc/op delta
Rand_New 32.0B ± 0% 48.0B ± 0% +50.00% (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)
Compared to math/rand:
Intel(R) Xeon(R) CPU E5-2680 v3 @ 2.50GHz, linux/amd64
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:
Intel(R) Xeon(R) CPU E5-2680 v3 @ 2.50GHz, linux/amd64
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.
Intel(R) Xeon(R) CPU E5-2680 v3 @ 2.50GHz, linux/amd64
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)
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.
If you are not counting nanoseconds, use math/rand/v2, it is a huge improvement
over nearly all aspects of math/rand. If you are counting nanoseconds,
pgregory.net/rand is still faster, without compromising quality.
This package builds on 4 primitives: raw 64-bit generation using sfc64 or goroutine-local
runtime.fastrand64(), floating-point generation using floating-point multiplication,
and integer generation in range using 32.64 or 64.128 fixed-point multiplication.
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.
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?
Top-level Seed() would require sharing global mutex-protected state between all top-level
functions, which (unlike the goroutine-local state used) does not scale when the parallelism increases.
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.
A bit slow. Otherwise, pcg64dxsm
is probably a fine choice.
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?
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.
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.
On most modern platforms, top-level functions in this package use goroutine-local wyrand
generators provided by the runtime as the best compromise between quality and speed
of concurrent execution.
Very fast, but relatively new and untested. Also, no guarantees about the period length.
pgregory.net/rand is stable. In addition to API stability, deterministic pseudo-random
generation produces the same results on 32-bit and 64-bit architectures, both little-endian
and big-endian. Any observable change to these results would only occur together
with a major version bump.
pgregory.net/rand is licensed under the Mozilla Public License Version 2.0.