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Common techniques for managing function state in C concurrent programming include: Thread-local storage (TLS) allows each thread to maintain its own independent copy of variables. Atomic variables allow atomic reading and writing of shared variables in a multi-threaded environment. Mutexes ensure state consistency by preventing multiple threads from executing critical sections at the same time.
C functions perform state management in concurrent programming
In multi-threaded programming, concurrent functions often need to manage their own state . To ensure data consistency and correctness, state management is crucial. This article explores common techniques for managing function state in C concurrent programming.
Thread Local Storage (TLS)
TLS allows each thread to have its own independent copy of a variable. This is useful for functions that need to maintain specific state for each thread. Here is an example of using TLS:
#include <thread> // 定义线程局部变量 thread_local int thread_counter; // 并发函数 void increment_counter() { ++thread_counter; std::cout << "Current counter: " << thread_counter << std::endl; } int main() { // 创建多个线程并执行并发函数 std::vector<std::thread> threads; for (int i = 0; i < 10; ++i) { threads.emplace_back(increment_counter); } // 等待所有线程完成 for (auto& thread : threads) { thread.join(); } return 0; }
Atomic Variables
Atomic variables allow atomic reading and writing of shared variables in a multi-threaded environment. This prevents race conditions and data corruption of the state. Here's how to use std::atomicbd43222e33876353aff11e13a7dc75f6 atomic variables:
#include <atomic> // 定义原子变量 std::atomic<int> counter; // 并发函数 void increment_counter() { ++counter; std::cout << "Current counter: " << counter << std::endl; } int main() { // 创建多个线程并执行并发函数 std::vector<std::thread> threads; for (int i = 0; i < 10; ++i) { threads.emplace_back(increment_counter); } // 等待所有线程完成 for (auto& thread : threads) { thread.join(); } return 0; }
Mutex lock
Mutex locks are used to control access to shared resources. They ensure state consistency by preventing multiple threads from executing critical sections simultaneously. Here's how to use a std::mutex mutex:
#include <mutex> // 定义互斥锁 std::mutex counter_lock; // 并发函数 void increment_counter() { // 获得锁 std::lock_guard<std::mutex> lock(counter_lock); // 读写共享状态 ++counter; std::cout << "Current counter: " << counter << std::endl; } int main() { // 创建多个线程并执行并发函数 std::vector<std::thread> threads; for (int i = 0; i < 10; ++i) { threads.emplace_back(increment_counter); } // 等待所有线程完成 for (auto& thread : threads) { thread.join(); } return 0; }
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