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)
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.
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; }
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, 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, 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(); }
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]; }
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