What is the application of STL function objects in handling concurrent programming?

In concurrent programming, STL function objects can simplify parallel processing through the following applications: Parallel task processing: Encapsulate function objects into tasks that can be executed in parallel. Queue processing: Store function objects and schedule them to different threads. Event handling: Register the function object as an event listener and execute it when the event is triggered.

The application of STL function objects in concurrent programming

In concurrent programming, the processing of function objects is complex and time-consuming Provides powerful tools for your tasks. The STL library provides a rich collection of function objects that simplify parallel processing and improve code readability and maintainability.

Function object

A function object is a class or structure that implements operator() or call. They behave like normal functions but can be passed, stored, and manipulated as objects.

Applications in concurrent programming

In concurrent programming, function objects can be used for:

• Parallel task processing: Encapsulate function objects into tasks that can be executed in parallel by using std::thread or std::async.
• Queue handling: Use std::queue to store function objects and schedule them as tasks to different threads.
• Event handling: Register a function object as an event listener to execute when a specific event is triggered.

Practical case: Parallel array summation

Consider a case of parallel calculation of the sum of arrays. You can use the following function object to perform parallel partitioning and summing of arrays:

struct SumPartition {
    int operator()(int start, int end) {
        int sum = 0;
        for (int i = start; i < end; ++i) {
            sum += array[i];
        }
        return sum;
    }

    int* array;
};

The following code demonstrates how to use this function object to perform parallel array summing:

#include <iostream>
#include <thread>
#include <vector>

using namespace std;

int main() {
    // 输入数组
    vector<int> array = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};

    // 分区大小
    int partitionSize = 2;

    // 创建线程池
    vector<thread> threads;
    int numPartitions = array.size() / partitionSize;

    // 启动并行求和
    for (int i = 0; i < numPartitions; ++i) {
        int start = i * partitionSize;
        int end = start + partitionSize;
        threads.emplace_back(thread(SumPartition(), start, end, array.data()));
    }

    // 等待线程完成
    for (auto& thread : threads) {
        thread.join();
    }

    // 计算最终结果
    int totalSum = 0;
    for (int i = 0; i < numPartitions; ++i) {
        totalSum += SumPartition()(i * partitionSize, i * partitionSize + partitionSize, array.data());
    }

    cout << "Total sum: " << totalSum << endl;

    return 0;
}

By using the STL function object, you can easily Array sum operations are effectively parallelized, thereby improving overall performance.

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