In this blog post, we will explore how to leverage the power of parallelism using the std::jthread class in C++. Time series analysis involves analyzing data points collected over a period of time to uncover patterns, trends, and relationships. By parallelizing the analysis, we can significantly accelerate the computation process and handle larger datasets more efficiently.

What is std::jthread?

Introduced in C++20, std::jthread is a synchronization primitive that represents an operating system thread of execution. It provides a convenient wrapper around the underlying thread object and takes care of managing the thread’s lifecycle, including joining or detaching it upon destruction.

Prerequisites

To follow along with the code examples in this blog post, ensure that you have a working knowledge of C++20 or a newer version, as well as a compatible compiler.

Parallel Time Series Analysis

Suppose we have a large time series dataset and want to calculate the moving average for each data point. The moving average is calculated by taking the average of a specified number of previous data points and the current point. This computation can be naturally parallelized as each moving average calculation is independent of others.

Let’s look at an example program that demonstrates the parallel computation of moving averages for time series data.

#include <iostream>
#include <vector>
#include <numeric>
#include <ranges>
#include <thread>

std::vector<double> calculateMovingAverages(const std::vector<double>& data, std::size_t windowSize) {
    std::vector<double> movingAverages;
    movingAverages.reserve(data.size());

    auto calculate = [&](std::size_t index) {
        auto begin = data.begin() + (index > windowSize ? index - windowSize : 0);
        auto end = data.begin() + index + 1;
        
        double average = std::accumulate(begin, end, 0.0) / std::distance(begin, end);
        movingAverages[index] = average;
    };

    std::vector<std::jthread> threads;
    threads.reserve(data.size());

    for (std::size_t i = 0; i < data.size(); ++i) {
        threads.emplace_back(calculate, i);
    }

    return movingAverages;
}

int main() {
    std::vector<double> data = { 1.0, 2.0, 3.0, 4.0, 5.0 };
    std::vector<double> movingAverages = calculateMovingAverages(data, 2);
    
    for (const auto& value : movingAverages) {
        std::cout << value << " ";
    }
    
    std::cout << std::endl;

    return 0;
}

In this example, the calculateMovingAverages function calculates the moving averages for a given data vector and windowSize. It creates a separate std::jthread for each data point and computes the moving average in parallel.

The lambda function calculate takes care of the moving average calculation for a specific index. It uses the std::accumulate algorithm to sum the window of data points and then divides it by the number of points to get the average. The result is stored in the movingAverages vector at the corresponding index.

The main function demonstrates the usage of the calculateMovingAverages function with a sample data vector and a window size of 2. It prints the calculated moving averages to the console.

Conclusion

The std::jthread class provides a simple and efficient way to introduce parallelism into time series analysis or any other computationally intensive task. By leveraging parallelism, we can significantly improve the performance of our programs and handle larger datasets with ease.

Remember to compile your code with the necessary compiler flags to enable C++20 features. Parallelizing time series analysis is just one example of the many possibilities that std::jthread enables. Experimenting with different algorithms and techniques can further optimize your code for parallel execution.

#cpp #C++Parallelism