Detailed explanation of C++ function optimization: How to optimize multi-threaded performance?

Key techniques for optimizing the performance of multi-threaded C functions include: compiler optimization flags (such as -O3 and -parallel) concurrent containers (such as std::vector and std::list) synchronization primitives (such as locks and atomic variables) Smart pointers (such as std::shared_ptr and std::unique_ptr) avoid lock contention (such as by using fine-grained locks or lock-free data structures)

Detailed explanation of C function optimization : How to optimize multi-threaded performance?

In multi-threaded programming, optimizing the performance of functions is crucial. This article will explore various techniques for optimizing multi-threaded performance of C functions and provide practical examples to illustrate.

Compiler optimization flags

The compiler provides a variety of optimization flags that can help optimize multi-threaded code. For example, the -O3 flag enables GCC's advanced optimizations, while the -parallel flag instructs the compiler to use parallelism.

Practical case:

// 启用优化标志
#pragma GCC optimize("O3", "-parallel")

// 优化函数
int sum(const std::vector<int>& numbers) {
  int result = 0;
  for (int number : numbers) {
    result += number;
  }
  return result;
}

Concurrent container

The C standard library provides concurrent containers, such as std::vector and std::list, these containers are optimized and can be safely used in multi-threaded scenarios.

Practical case:

// 使用并发容器
std::vector<int> numbers(1000000);
std::atomic<int> result;

// 并发地累加数字
std::thread threads[8];
for (int i = 0; i < 8; i++) {
  threads[i] = std::thread([&numbers, &result, i]() {
    for (int j = i * numbers.size() / 8; j < (i + 1) * numbers.size() / 8; j++) {
      result += numbers[j];
    }
  });
}

for (int i = 0; i < 8; i++) {
  threads[i].join();
}

// 获取最终结果
int final_result = result.load();

Synchronization primitives

Synchronization primitives, such as locks and atomic variables, are used to coordinate access between multiple threads . Appropriate use of these primitives can ensure data consistency and avoid race conditions.

Practical case:

// 使用互斥量保护共享数据
std::mutex m;
int shared_data = 0;

// 使用互斥量并发地更新共享数据
std::thread threads[8];
for (int i = 0; i < 8; i++) {
  threads[i] = std::thread([&m, &shared_data, i]() {
    for (int j = 0; j < 1000; j++) {
      std::lock_guard<std::mutex> lock(m);
      shared_data += i;
    }
  });
}

for (int i = 0; i < 8; i++) {
  threads[i].join();
}

// 获取最终结果
int final_result = shared_data;

Smart pointer

Smart pointer, such as std::shared_ptr and std: :unique_ptr, which can automatically manage dynamically allocated memory. They support safe sharing and release in multi-threaded scenarios.

Practical case:

// 使用智能指针共享对象
std::shared_ptr<MyObject> object = std::make_shared<MyObject>();

// 在多个线程中并发访问共享对象
std::thread threads[8];
for (int i = 0; i < 8; i++) {
  threads[i] = std::thread([&object, i]() {
    std::cout << object->getValue() << std::endl;
  });
}

for (int i = 0; i < 8; i++) {
  threads[i].join();
}

Avoid lock contention

Lock contention refers to the situation where multiple threads frequently compete for the same lock. Lock contention can be avoided by using fine-grained locks or lock-free data structures.

Practical case:

// 使用细粒度锁避免锁争用
std::mutex locks[10];
int shared_data[10];

// 并发地更新共享数据，每个数据块使用自己的锁
std::thread threads[8];
for (int i = 0; i < 8; i++) {
  threads[i] = std::thread([&locks, &shared_data, i]() {
    for (int j = 0; j < 1000; j++) {
      std::lock_guard<std::mutex> lock(locks[i]);
      shared_data[i] += i;
    }
  });
}

for (int i = 0; i < 8; i++) {
  threads[i].join();
}

// 获取最终结果
int final_result = 0;
for (int i = 0; i < 10; i++) {
  final_result += shared_data[i];
}

The above is the detailed content of Detailed explanation of C++ function optimization: How to optimize multi-threaded performance?. For more information, please follow other related articles on the PHP Chinese website!