How does multithreading in C++ affect function performance?

The impact of multi-threading on function performance: Thread creation/destruction overhead: consuming system resources and affecting performance. Thread synchronization: avoids data corruption, but increases overhead. Context switching overhead: The system incurs when switching between threads. Practical case: Fibonacci sequence calculation, multi-threaded parallel computing can improve performance.

The impact of the multi-threading mechanism in C on function performance

Multi-threading refers to running multiple program fragments at the same time Ability. In C, multithreading is implemented through the std::thread class.

When a function is run in a multi-threaded environment, its performance may be affected by the following factors:

• ##Thread creation and destruction overhead: Creation and Destroying threads requires system resources, which may have an impact on the performance of the function, especially when the number of threads is large.
• Thread synchronization: When threads access shared resources, a synchronization mechanism is required to avoid data corruption. This can be achieved through synchronization primitives such as locks, mutexes, and condition variables, but it also adds overhead to the function.
• Context switching overhead: When the system switches between different threads, context switching overhead will occur. This includes saving and restoring registers and other processor state. Frequent context switching may significantly reduce the performance of a function.

Practical case:

Consider the following function that calculates the Fibonacci sequence:

int fibonacci(int n) {
    if (n <= 1) {
        return n;
    } else {
        return fibonacci(n - 1) + fibonacci(n - 2);
    }
}

When running in a single-threaded environment , the performance of this function decreases exponentially as

n increases. This is because the function calls itself recursively, causing a lot of context switching overhead.

In order to improve performance, we can use multi-threading to calculate Fibonacci numbers in parallel. Here is the minimized multi-threaded version:

#include <thread>

int fibonacci_thread(int n) {
    if (n <= 1) {
        return n;
    } else {
        std::thread t1(fibonacci_thread, n - 1);
        std::thread t2(fibonacci_thread, n - 2);
        t1.join();
        t2.join();
        return t1.get() + t2.get();
    }
}

In this example, we use two threads to calculate

fibonacci(n - 1) and fibonacci(n - 2 ). This reduces the number of recursive calls, thereby reducing context switching overhead.

By using multi-threading, we can significantly improve the performance of the fibonacci function, especially when

n values ​​are large.

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