mirror of https://github.com/VictoriaMetrics/VictoriaMetrics.git synced 2024-12-21 07:56:26 +01:00

History

Aliaksandr Valialkin c018f47e81 go.mod: update minimum Go version from Go 1.16 to Go 1.17 VictoriaMetrics code uses features from Go 1.17, so the minimum Go version must be increased from Go 1.16 to Go 1.17 See https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1987		2021-12-22 12:39:22 +02:00
..
.travis.yml	all: open-sourcing single-node version	2019-05-23 00:18:06 +03:00
fastrand.go	vendor: update github.com/valyala/histogram from v1.1.2 to v1.2.0	2021-09-15 09:35:40 +03:00
LICENSE	all: open-sourcing single-node version	2019-05-23 00:18:06 +03:00
README.md	all: open-sourcing single-node version	2019-05-23 00:18:06 +03:00

README.md

fastrand

Fast pseudorandom number generator.

Features

Optimized for speed.
Performance scales on multiple CPUs.

How does it work?

It abuses sync.Pool for maintaining "per-CPU" pseudorandom number generators.

TODO: firgure out how to use real per-CPU pseudorandom number generators.

Benchmark results

$ GOMAXPROCS=1 go test -bench=. github.com/valyala/fastrand
goos: linux
goarch: amd64
pkg: github.com/valyala/fastrand
BenchmarkUint32n                   	50000000	        29.7 ns/op
BenchmarkRNGUint32n                	200000000	         6.50 ns/op
BenchmarkRNGUint32nWithLock        	100000000	        21.5 ns/op
BenchmarkMathRandInt31n            	50000000	        31.8 ns/op
BenchmarkMathRandRNGInt31n         	100000000	        17.9 ns/op
BenchmarkMathRandRNGInt31nWithLock 	50000000	        30.2 ns/op
PASS
ok  	github.com/valyala/fastrand	10.634s

$ GOMAXPROCS=2 go test -bench=. github.com/valyala/fastrand
goos: linux
goarch: amd64
pkg: github.com/valyala/fastrand
BenchmarkUint32n-2                     	100000000	        17.6 ns/op
BenchmarkRNGUint32n-2                  	500000000	         3.36 ns/op
BenchmarkRNGUint32nWithLock-2          	50000000	        32.0 ns/op
BenchmarkMathRandInt31n-2              	20000000	        51.2 ns/op
BenchmarkMathRandRNGInt31n-2           	100000000	        11.0 ns/op
BenchmarkMathRandRNGInt31nWithLock-2   	20000000	        91.0 ns/op
PASS
ok  	github.com/valyala/fastrand	9.543s

$ GOMAXPROCS=4 go test -bench=. github.com/valyala/fastrand
goos: linux
goarch: amd64
pkg: github.com/valyala/fastrand
BenchmarkUint32n-4                     	100000000	        14.2 ns/op
BenchmarkRNGUint32n-4                  	500000000	         3.30 ns/op
BenchmarkRNGUint32nWithLock-4          	20000000	        88.7 ns/op
BenchmarkMathRandInt31n-4              	10000000	       145 ns/op
BenchmarkMathRandRNGInt31n-4           	200000000	         8.35 ns/op
BenchmarkMathRandRNGInt31nWithLock-4   	20000000	       102 ns/op
PASS
ok  	github.com/valyala/fastrand	11.534s

As you can see, fastrand.Uint32n scales on multiple CPUs, while rand.Int31n doesn't scale. Their performance is comparable on GOMAXPROCS=1, but fastrand.Uint32n runs 3x faster than rand.Int31n on GOMAXPROCS=2 and 10x faster than rand.Int31n on GOMAXPROCS=4.