Data visualization plays a crucial role in understanding complex datasets. As datasets grow in size, visualizing them can become computationally expensive and time-consuming. To overcome these challenges, parallel programming techniques can be employed. In this blog post, we will explore how to implement parallel data visualization using std::jthread in C++.

What is std::jthread?

Introduced in C++20, std::jthread is a class that represents an active thread of execution. It is similar to std::thread but provides additional functionality such as cooperative cancellation and easy integration with the RAII (Resource Acquisition Is Initialization) idiom. It can be a powerful tool for writing parallel programs in a straightforward manner.

Parallel Data Visualization Example

Let’s consider an example where we have a large dataset and want to visualize it using a scatter plot. For simplicity, let’s assume our dataset is a 2D vector containing x and y coordinates:

#include <iostream>
#include <vector>
#include <random>

std::vector<std::pair<float, float>> generateRandomData(size_t dataSize) {
    std::vector<std::pair<float, float>> data;
    std::random_device rd;
    std::mt19937 gen(rd());
    std::uniform_real_distribution<float> dis(0, 100);

    for (size_t i = 0; i < dataSize; ++i) {
        float x = dis(gen);
        float y = dis(gen);
        data.emplace_back(x, y);
    }

    return data;
}

void visualizeData(const std::vector<std::pair<float, float>>& data) {
    // Code to visualize data
    // ...
}

int main() {
    std::vector<std::pair<float, float>> data = generateRandomData(1000000);
    visualizeData(data);
    // ...
    return 0;
}

In this example, we generate a large random dataset using the generateRandomData function. Then, we pass this dataset to the visualizeData function for visualization.

To parallelize the visualization process, we can utilize std::jthread. Here’s an updated version of visualizeData using parallelism:

#include <iostream>
#include <vector>
#include <random>
#include <thread>
#include <algorithm>

void visualizeData(const std::vector<std::pair<float, float>>& data) {
    // Determine the number of available threads
    const unsigned int numThreads = std::thread::hardware_concurrency();

    // Split the data into smaller chunks based on the number of threads
    const size_t chunkSize = data.size() / numThreads;

    // Function to visualize a chunk of data
    auto visualizeChunk = [](const std::vector<std::pair<float, float>>& chunk) {
        // Code to visualize a chunk of data
        // ...
    };

    // Create a vector of jthreads
    std::vector<std::jthread> threads;

    // Launch jthreads to visualize chunks of data in parallel
    for (unsigned int i = 0; i < numThreads; ++i) {
        // Determine the range of data for the current thread
        auto begin = data.begin() + i * chunkSize;
        auto end = (i == numThreads - 1) ? data.end() : begin + chunkSize;

        // Launch a jthread with the visualization task
        threads.emplace_back(visualizeChunk, std::vector<std::pair<float, float>>(begin, end));
    }

    // Wait for all jthreads to finish
    std::for_each(threads.begin(), threads.end(), [](std::jthread& jthread) {
        jthread.join();
    });
}

int main() {
    std::vector<std::pair<float, float>> data = generateRandomData(1000000);
    visualizeData(data);
    // ...
    return 0;
}

In the updated code, we first determine the number of available threads using std::thread::hardware_concurrency(). We then split the data vector into smaller chunks based on this number.

Next, we define the visualizeChunk lambda function, which takes a chunk of data as input and visualizes it. Each std::jthread is launched with this lambda function and the corresponding chunk of data.

Finally, we wait for all the threads to finish using std::jthread::join(). This ensures that the visualization process is completed before moving forward.

Conclusion

By utilizing std::jthread, we can easily implement parallel data visualization in C++. This technique allows us to take advantage of multiple CPU cores and speed up the visualization process for large datasets. Parallel programming, when used correctly, can be a powerful tool for boosting the performance of data visualization applications.

#dataVisualization #parallelProgramming