上代码演示下Profile-Guided Optimization (PGO)

Shell脚本名叫step

#!/bin/bashif [[ $# -ne 1 ]]; then exit; firun() { C="g++ $1 main.cpp"; echo $C; $C; a.out; }case $1 in
'0') run '' ;;
'1') run '-fprofile-generate=.' ;;
'2') run '-fprofile-use=.' ;;
# -march=native不是默认选项，需显式指定，其作用是根据当前编译机器
# 的CPU自动启用支持的指令集优化。
'3') run '-O3 -march=native' ;;
esac

main.cpp

#include <chrono> // C++11时间库
#include <random>
#include <iostream>
using namespace std;auto now = chrono::high_resolution_clock::now;// 模拟热点分支（PGO优化重点）
int process (int x) {if (x > 100) // 该条件90%概率成立（模拟热点分支）return x * 2;return x + 1;
}int main () {random_device rd;auto seed = rd();mt19937 gen(seed);//生成伯努利分布随机布尔值，90% truebernoulli_distribution dist(0.9);auto start = now();int sum = 0;for (int i = 0; i < 1'000'000; ++i)sum += process(dist(gen) ? 150 : 50);// F U C K !cout << chrono::duration<double>(now() - start).count() << "s\n";exit(sum);
}

运行 (Intel N100)

~/pgo$ step 0
g++ main.cpp
0.0532354s
~/pgo$ step 1
g++ -fprofile-generate=. main.cpp
0.060655s
~/pgo$ step 2
g++ -fprofile-use=. main.cpp
0.0532936s
~/pgo$ step 3
g++ -O3 -march=native main.cpp
0.00564738s

colinsblog说：I experimented with some existing C++ 14 applications I’ve written. One, a flat-file to Parquet format converter, improved by only about five percent over an executable built with blanket -O3 optimization levels. Another, the “DCP” I’ve discussed before, improved by around thirty percent faster compared with the same program built with -O3. These tests were done with GCC 5.4, not exactly the newest. I’ll attempt to do similar tests with GCC 7 and 9.

我用的是gcc version 12.2.0 (Debian 12.2.0-14)

AI说：PGO是一种编译器优化技术，通过分析程序实际运行数据（如函数调用频率、分支路径等）生成配置文件，指导编译器对热点代码进行精准优化。其核心流程包括：插桩编译→数据采集→二次优化编译。PGO能显著提升性能，例如减少分支预测错误、优化代码布局以提高缓存命中率，在Chrome等应用中实现10%-20%的性能提升。该技术适用于计算密集型场景，需配合代表性数据收集以发挥最大效果。

AI好啊，讲PGO的中文网页基本上没有代码，而AI能编演示程序，写Makefile，虽然把-fprofile-generate和use的参数当成了文件名，导致use时说找不到文件。

补丁：step 1前得先rm *.gcda.

改成15'000'000后，0.799088s, 0.906551s, 0.800046s, 0.0843707s