Ray tracing is a popular technique used in computer graphics to generate realistic images by simulating the behavior of light rays. However, due to its computationally intensive nature, ray tracing can be time-consuming and resource-intensive. To overcome these limitations, parallelizing the ray tracing process can be incredibly beneficial.

In this blog post, we will explore how to implement parallel ray tracing using std::jthread, a new feature introduced in C++20 that provides a simpler and more intuitive way to manage threads. So let’s dive into the code and see how we can leverage parallelism to speed up our ray tracing algorithm.

Setting Up the Scene

Before diving into the parallelization aspect, let’s first set up a simple scene for our ray tracing example. For simplicity, we’ll create a basic scene with a few objects and a camera.

#include <iostream>
#include <vector>

// Define classes for objects, camera, etc.

int main() {
    // Create objects and set up the scene

    // Perform ray tracing

    return 0;
}

Parallelizing Ray Tracing

To parallelize the ray tracing process, we can divide the screen into smaller tiles and assign each thread the task of rendering a specific tile. To achieve this, we’ll utilize std::jthread, which simplifies the management of threads by combining thread creation and joining into a single step.

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

// Define classes for objects, camera, etc.

int main() {
    // Create objects and set up the scene

    const int numThreads = std::thread::hardware_concurrency(); // Get the number of available threads

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

    const int tileWidth = screenWidth / numThreads; // Calculate the width of each tile

    for (int i = 0; i < numThreads; ++i) {
        int startX = i * tileWidth; // Calculate the starting x-coordinate of the tile

        std::jthread thread([startX, tileWidth, &scene]() {
            // Perform ray tracing for the assigned tile
        });

        threads.push_back(std::move(thread));
    }

    for (auto& thread : threads) {
        thread.join(); // Wait for all threads to finish
    }

    return 0;
}

In the above code snippet, we first determine the number of available threads using std::thread::hardware_concurrency(). We then create a vector to store std::jthread objects, one for each thread.

Next, we calculate the width of each tile based on the screen width and the number of threads. We iterate over the number of threads, calculating the starting x-coordinate of each tile and creating a new std::jthread object for that tile. Inside the thread, we perform ray tracing for the assigned tile.

Finally, we wait for all threads to finish by calling join() on each std::jthread object.

Conclusion

Parallelizing ray tracing can significantly speed up its execution time, allowing for more complex and realistic graphics. With the introduction of std::jthread in C++20, managing threads has become much simpler and more readable.

In this blog post, we explored how to implement parallel ray tracing using std::jthread. By dividing the screen into smaller tiles and assigning them to separate threads, we can take advantage of parallelism and boost the performance of our ray tracing algorithm.

#raytracing #parallelcomputing