受SQLite启发,我正在考虑使用valgrind的"cachegrind"工具来进行可重复的性能基准测试。它输出的数字比我找到的任何其他计时方法都要稳定得多,但它们仍然不是确定性的。以下是一个简单的C程序示例:
int main() {
volatile int x;
while (x < 1000000) {
x++;
}
}
如果我在cachegrind下编译并运行它,我会得到以下结果:
$ gcc -O2 x.c -o x
$ valgrind --tool=cachegrind ./x
==11949== Cachegrind, a cache and branch-prediction profiler
==11949== Copyright (C) 2002-2015, and GNU GPL'd, by Nicholas Nethercote et al.
==11949== Using Valgrind-3.11.0.SVN and LibVEX; rerun with -h for copyright info
==11949== Command: ./x
==11949==
--11949-- warning: L3 cache found, using its data for the LL simulation.
==11949==
==11949== I refs: 11,158,333
==11949== I1 misses: 3,565
==11949== LLi misses: 2,611
==11949== I1 miss rate: 0.03%
==11949== LLi miss rate: 0.02%
==11949==
==11949== D refs: 4,116,700 (3,552,970 rd + 563,730 wr)
==11949== D1 misses: 21,119 ( 19,041 rd + 2,078 wr)
==11949== LLd misses: 7,487 ( 6,148 rd + 1,339 wr)
==11949== D1 miss rate: 0.5% ( 0.5% + 0.4% )
==11949== LLd miss rate: 0.2% ( 0.2% + 0.2% )
==11949==
==11949== LL refs: 24,684 ( 22,606 rd + 2,078 wr)
==11949== LL misses: 10,098 ( 8,759 rd + 1,339 wr)
==11949== LL miss rate: 0.1% ( 0.1% + 0.2% )
$ valgrind --tool=cachegrind ./x
==11982== Cachegrind, a cache and branch-prediction profiler
==11982== Copyright (C) 2002-2015, and GNU GPL'd, by Nicholas Nethercote et al.
==11982== Using Valgrind-3.11.0.SVN and LibVEX; rerun with -h for copyright info
==11982== Command: ./x
==11982==
--11982-- warning: L3 cache found, using its data for the LL simulation.
==11982==
==11982== I refs: 11,159,225
==11982== I1 misses: 3,611
==11982== LLi misses: 2,611
==11982== I1 miss rate: 0.03%
==11982== LLi miss rate: 0.02%
==11982==
==11982== D refs: 4,117,029 (3,553,176 rd + 563,853 wr)
==11982== D1 misses: 21,174 ( 19,090 rd + 2,084 wr)
==11982== LLd misses: 7,496 ( 6,154 rd + 1,342 wr)
==11982== D1 miss rate: 0.5% ( 0.5% + 0.4% )
==11982== LLd miss rate: 0.2% ( 0.2% + 0.2% )
==11982==
==11982== LL refs: 24,785 ( 22,701 rd + 2,084 wr)
==11982== LL misses: 10,107 ( 8,765 rd + 1,342 wr)
==11982== LL miss rate: 0.1% ( 0.1% + 0.2% )
$
在这种情况下,“I refs”在两次运行之间仅有0.008%的差异,但我仍然想知道为什么它们不同。在更复杂的程序中(几十毫秒),它们可能会有更大的差异。是否有方法使运行完全可再生?