（三四）借助可视化调优工具：实时显示编译优化效果以提升应用性能 原创

import time                import matplotlib.pyplot as plt                                 # 模拟一个简单的函数                def expensive_function():                    start_time = time.time()                    # 模拟一些耗时操作                    for _ in range(1000000):                        pass                    end_time = time.time()                    return end_time - start_time                                 # 收集性能数据                execution_times = []                for _ in range(5):                    execution_time = expensive_function()                    execution_times.append(execution_time)                                 # 可视化性能数据                plt.plot(range(1, 6), execution_times)                plt.xlabel('Run Number')                plt.ylabel('Execution Time (s)')                plt.title('Function Execution Time')                plt.show()
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#include <stdio.h>                                 // 一个简单的函数                int sum(int n) {                    int result = 0;                    for (int i = 1; i <= n; i++) {                        result += i;                    }                    return result;                }                                 int main() {                    int n = 1000;                    int result = sum(n);                    printf("The sum from 1 to %d is %d\n", n, result);                    return 0;                }
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# 不进行优化                gcc -o program_no_opt program.c                                 # 使用 -O1 优化级别                gcc -O1 -o program_opt1 program.c                                 # 使用 -O2 优化级别                gcc -O2 -o program_opt2 program.c
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