C++ Performance Optimization Guide: Discover the secrets to making your code more efficient

C++ performance optimization involves a variety of techniques, including: 1. Avoiding dynamic allocation; 2. Using compiler optimization flags; 3. Selecting optimized data structures; 4. Application caching; 5. Parallel programming. The optimization practical case shows how to apply these techniques when finding the longest ascending subsequence in an integer array, improving the algorithm efficiency from O(n^2) to O(n log n).

C++ Performance Optimization Guide: Master the secret to improving code execution efficiency

Introduction

C++ is a powerful programming language known for its speed and efficiency. By implementing a few key optimization techniques, you can significantly improve the performance of your C++ code. This article takes an in-depth look at C++ performance optimization and provides practical examples to illustrate each technique.

Optimization technology

1. Avoid dynamic allocation and release

Dynamic allocation and release of memory is very time-consuming, especially When executed frequently. Try to use local variables and smart pointers to avoid dynamic memory management overhead.

// 动态分配
int* arr = new int[100];
delete[] arr; // 释放内存

// 智能指针（避免手动释放）
std::unique_ptr<int[]> arr(new int[100]);

2. Use compiler optimization flags

The compiler provides various optimization flags that can be used to generate faster code. Turn on these flags to improve function inlining, loop optimization, and code generation quality.

// GCC 编译器
g++ -O3 -Wall -std=c++17

3. Optimize data structures

It is crucial to choose a data structure that suits your specific needs. For example, for fast lookup, use a hash table instead of a linear search.

// 哈希表（快速查找）
std::unordered_map<int, int> myMap;

4. Apply caching technology

Cache stores frequently accessed data, thereby avoiding expensive calculations or I/O operations. Implement caching using smart pointers or generic containers.

// LRU 缓存（最近最少使用）
class LRUCache {
    std::unordered_map<int, int> cache;
    std::list<int> lru;
public:
    int get(int key) { ... }
    void put(int key, int value) { ... }
};

5. Parallel Programming

For CPU-intensive tasks, using multi-threaded parallel programming can improve performance. The standard library provides threading and synchronization primitives.

// 并行 for 循环
#include <thread>
#include <vector>

void parallel_sum(const std::vector<int>& arr, int* sum) {
    std::vector<std::thread> threads;
    int thread_count = std::thread::hardware_concurrency();
    for (int i = 0; i < thread_count; i++) {
        threads.push_back(std::thread([=, &arr, &sum] {
            *sum += std::accumulate(&arr[i * arr.size() / thread_count],
                                   &arr[(i + 1) * arr.size() / thread_count], 0);
        }));
    }
    for (auto& thread : threads) thread.join();
}

Practical case

Case: Find the longest ascending subsequence in an integer array

// 未优化的算法（时间复杂度：O(n^2)）
int longestSubsequence(const std::vector<int>& arr) {
    int maxLength = 0;
    for (int i = 0; i < arr.size(); i++) {
        for (int j = i + 1; j < arr.size(); j++) {
            if (arr[j] > arr[i]) maxLength = std::max(maxLength, j - i + 1);
        }
    }
    return maxLength;
}

// 优化后的算法（时间复杂度：O(n log n)）
int longestSubsequence(const std::vector<int>& arr) {
    std::vector<int> tail(arr.size(), -1);
    int maxIndex = -1;
    for (int i = 0; i < arr.size(); i++) {
        // 二分搜索查找插入点
        int l = 0, r = maxIndex;
        while (l < r) {
            int mid = (l + r) / 2;
            if (arr[tail[mid]] < arr[i]) l = mid + 1;
            else r = mid;
        }
        if (maxIndex < r) {
            if (r == 0) tail[0] = i;
            else tail[r] = i;
            maxIndex = r;
        }
    }
    return maxIndex + 1;
}

By using binary search Optimizing the recursion process, the efficiency of the algorithm increases from O(n^2) to O(n log n).

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