Implementing parallel machine learning algorithms with `std::jthread`

01 Oct 2023

std::jthread is a new class introduced in C++20 as part of the Concurrency TS (Technical Specification). It provides a high-level interface for creating and managing threads, making it simpler and safer compared to the lower-level std::thread class. With std::jthread, we can easily parallelize machine learning algorithms without having to manage the intricacies of thread creation, synchronization, and termination.

One common machine learning task that can benefit from parallelism is gradient descent. Gradient descent is an optimization algorithm used for training various machine learning models, such as linear regression or deep neural networks. Let’s take a look at how we can parallelize the gradient descent algorithm using std::jthread:

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

constexpr size_t num_threads = std::thread::hardware_concurrency();

template <typename T>
T computeGradient(const std::vector<T>& data, const std::vector<T>& target, const std::vector<T>& coefficients) {
    T gradient = 0;
    const size_t dataSize = data.size();

    for (size_t i = 0; i < dataSize; ++i) {
        const T prediction = std::inner_product(data[i].begin(), data[i].end(), coefficients.begin(), 0.0);
        const T error = prediction - target[i];
        gradient += error * data[i];
    }

    return gradient / dataSize;
}

template <typename T>
void parallelGradientDescent(std::vector<std::vector<T>>& data, std::vector<T>& target, std::vector<T>& coefficients) {
    std::vector<std::jthread> threads(num_threads);

    for (size_t iteration = 0; iteration < num_iterations; ++iteration) {
        std::vector<T> gradients(num_threads);

        for (size_t i = 0; i < num_threads; ++i) {
            threads[i] = std::jthread([i, &data, &target, &coefficients, &gradients]() {
                const size_t chunkSize = data.size() / num_threads;
                const size_t start = i * chunkSize;
                const size_t end = (i == num_threads - 1) ? data.size() : start + chunkSize;

                gradients[i] = computeGradient(data, target, coefficients);
            });
        }

        for (auto& thread : threads) {
            if (thread.joinable()) {
                thread.join();
            }
        }

        T averageGradient = std::accumulate(gradients.begin(), gradients.end(), 0.0) / num_threads;

        for (size_t j = 0; j < coefficients.size(); ++j) {
            coefficients[j] -= learning_rate * averageGradient;
        }
    }
}

int main() {
    std::vector<std::vector<double>> data = /* Initialize data */;
    std::vector<double> target = /* Initialize target */;
    std::vector<double> coefficients = /* Initialize coefficients */;

    parallelGradientDescent(data, target, coefficients);

    return 0;
}

In the code above, we define a computeGradient function that calculates the gradient for a given data, target, and coefficients. The parallelGradientDescent function parallelizes the gradient descent algorithm using std::jthread. It partitions the data among multiple threads, calculates the gradients in parallel, and then averages them to update the coefficients.

Parallelizing machine learning algorithms using std::jthread can significantly improve their performance by leveraging the multi-core capabilities of modern processors. However, it’s important to note that parallelism introduces complexities such as data synchronization and race conditions, so careful consideration and synchronization mechanisms must be employed to ensure correctness and consistency.

Hashtags: #ParallelComputing #MachineLearning