Parallel sorting algorithms can greatly improve performance by utilizing multiple threads to sort a given collection of data. In C++, the <algorithm> library provides convenient sorting functions, but they are not inherently parallel. However, with the introduction of C++20, the standard library now includes the <jthread> header, which allows for easy creation and management of threads.

In this blog post, we will explore how to implement parallel sorting algorithms using std::jthread and how they can enhance the performance of sorting large datasets.

Sorting Algorithms and Parallelism

Traditionally, sorting algorithms like Quicksort, Mergesort, and Heapsort operate sequentially, making them less efficient for large datasets. Parallel sorting algorithms take advantage of multiple threads to divide the workload and perform sorting in parallel, leading to significant performance improvements.

Parallel Sorting with std::jthread

To implement parallel sorting algorithms with std::jthread, we can divide the input dataset into smaller parts and sort them concurrently in different threads. Once the individual parts are sorted, we can merge them back into a single sorted collection.

Here’s an example of parallel Quicksort implemented using std::jthread:

#include <algorithm>
#include <vector>
#include <thread>
#include <ranges>

template<typename RandomIt>
void parallel_quicksort(RandomIt first, RandomIt last) {
    if (first == last) return;

    if (last - first < 1000) {
        std::ranges::sort(first, last);  // Sequential sort for smaller subarrays
        return;
    }

    RandomIt pivot = std::partition(first, last, [&](const auto& element) {
        return element < *first;
    });

    std::jthread left_thread([&]() { parallel_quicksort(first, pivot); });
    parallel_quicksort(pivot + 1, last);
}

int main() {
    std::vector<int> data = {9, 2, 5, 1, 7, 8, 3, 6, 4};
    parallel_quicksort(data.begin(), data.end());

    for (const auto& element : data) {
        std::cout << element << " ";
    }
    
    return 0;
}

In this example, the parallel_quicksort function checks the size of the input subarray and determines whether to sort it sequentially or in parallel. If the size is below a threshold (1000 in this case), it invokes std::ranges::sort to perform sequential sorting. Otherwise, it partitions the array using the first element as the pivot and creates a new std::jthread for sorting the left partition in parallel.

The code above demonstrates parallel Quicksort, but you can use the same principles to implement other sorting algorithms in parallel, such as Mergesort or Heapsort.

Improving Performance with Parallel Sorting

By leveraging multiple threads through std::jthread, parallel sorting algorithms can achieve significant performance improvements on large datasets. The division of work among multiple threads allows for faster sorting, reducing overall runtime.

However, it’s important to note that parallelism introduces overhead due to thread creation, synchronization, and merging of sorted parts. Therefore, the optimal performance gains may vary depending on factors such as the size of the dataset, the number of available CPU cores, and the nature of the sorting algorithm.

#tech #cpp #parallel #sorting