C++ coroutines have gained a lot of attention in recent years for their ability to simplify asynchronous programming. While they are primarily used in asynchronous scenarios, there are also use cases for coroutines in parallel computing. In this article, we will explore some of these use cases and understand how coroutines can enhance parallel programming in C++.

1. Task-based Parallelism

One of the key use cases of C++ coroutines in parallel computing is task-based parallelism. Coroutines provide a natural way to express tasks that can be executed concurrently. By using coroutines, developers can create lightweight tasks that can run in parallel and communicate with each other using awaitable objects.

Here’s an example of how coroutines can be used for task-based parallelism:

#include <iostream>
#include <vector>
#include <experimental/coroutine>

std::vector<int> data = {1, 2, 3, 4, 5};

struct task {
    struct promise_type {
        task get_return_object() { return {}; }
        std::experimental::suspend_never initial_suspend() { return {}; }
        std::experimental::suspend_never final_suspend() noexcept { return {}; }
        void return_void() {}
        void unhandled_exception() {}
    };
};

task perform_heavy_computation() {
    for (int i = 0; i < data.size(); i++) {
        // Perform heavy computation on data[i] asynchronously
        co_await std::experimental::suspend_always{};
        std::cout << "Processed " << data[i] << std::endl;
    }
}

int main() {
    perform_heavy_computation();
    return 0;
}

In this example, the perform_heavy_computation coroutine performs heavy computation on each element of the data vector asynchronously. The co_await keyword is used to suspend the coroutine until the computation is complete. This allows multiple computations to run in parallel, improving overall performance.

2. Data Parallelism

Another use case for C++ coroutines in parallel computing is data parallelism. Coroutines can be used to express parallel processing of data elements or chunks. By splitting the problem into smaller tasks and executing them in parallel, coroutines enable efficient data parallelism.

Consider the following code snippet that demonstrates data parallelism using coroutines:

#include <iostream>
#include <vector>
#include <experimental/coroutine>
#include <algorithm>

std::vector<int> data = {5, 3, 2, 4, 1};

struct task {
    struct promise_type {
        task get_return_object() { return {}; }
        std::experimental::suspend_never initial_suspend() { return {}; }
        std::experimental::suspend_never final_suspend() noexcept { return {}; }
        void return_void() {}
        void unhandled_exception() {}
    };
};

task sort_data() {
    std::sort(std::begin(data), std::end(data));
}

int main() {
    sort_data();
    
    std::cout << "Sorted data: ";
    for (const auto& element : data) {
        std::cout << element << " ";
    }
    std::cout << std::endl;
    
    return 0;
}

In this example, the sort_data coroutine sorts the data vector using std::sort algorithm. The coroutine automatically executes the sorting task in parallel, improving the performance of the sorting operation.

Overall, C++ coroutines have proven to be a powerful tool for simplifying asynchronous programming. In parallel computing, coroutines can be used for task-based parallelism and data parallelism, enabling efficient utilization of computational resources. With the ability to express lightweight tasks and handle concurrency seamlessly, coroutines add value to parallel programming in C++.

#cpluspluscoroutines #parallelcomputing