This post explains how to profile benchmarks with an example: Benchmark Profiling with pprof.
The following benchmark simulates some CPU work.
package main
import (
"math/rand"
"testing"
)
func BenchmarkRand(b *testing.B) {
for n := 0; n < b.N; n++ {
rand.Int63()
}
}
To generate a CPU profile for the benchmark test, run:
go test -bench=BenchmarkRand -benchmem -cpuprofile profile.out
The -memprofile and -blockprofile flags can be used to generate memory allocation and blocking call profiles.
To analyze the profile use the Go tool:
go tool pprof profile.out
(pprof) top
Showing nodes accounting for 1.16s, 100% of 1.16s total
Showing top 10 nodes out of 22
flat flat% sum% cum cum%
0.41s 35.34% 35.34% 0.41s 35.34% sync.(*Mutex).Unlock
0.37s 31.90% 67.24% 0.37s 31.90% sync.(*Mutex).Lock
0.12s 10.34% 77.59% 1.03s 88.79% math/rand.(*lockedSource).Int63
0.08s 6.90% 84.48% 0.08s 6.90% math/rand.(*rngSource).Uint64 (inline)
0.06s 5.17% 89.66% 1.11s 95.69% math/rand.Int63
0.05s 4.31% 93.97% 0.13s 11.21% math/rand.(*rngSource).Int63
0.04s 3.45% 97.41% 1.15s 99.14% benchtest.BenchmarkRand
0.02s 1.72% 99.14% 1.05s 90.52% math/rand.(*Rand).Int63
0.01s 0.86% 100% 0.01s 0.86% runtime.futex
0 0% 100% 0.01s 0.86% runtime.allocm
The bottleneck in this case is the mutex, caused by the default source in math/rand being synchronized.
Other profile presentations and output formats are also possible, e.g. tree. Type help for more options.
Note, that any initialization code before the benchmark loop will also be profiled.