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Parallel Computing with CUDA: A Practical Guide to Parallel Reduction


Parallel computing has revolutionised how we tackle complex computational problems. Leveraging the capabilities of Graphics Processing Units (GPUs) through NVIDIA’s CUDA platform enables us to perform tasks at unprecedented speeds. In this article, we’ll explore the practical implementation of a crucial CUDA technique: parallel reduction. We’ll walk through the code for a parallel reduction example and provide steps to compile and run it. Additionally, we’ll discuss the significance of parallel reduction and its real-world applications.

Understanding Parallel Reduction

Parallel reduction, also known as parallel summation, is a technique used to efficiently compute the sum (or other associative operations) of elements in an array through parallelism. It divides the task among multiple threads, drastically reducing computation time compared to sequential methods.

Code Example: Parallel Reduction

#include <iostream>
#include <cmath>

// CUDA kernel for parallel reduction
__global__ void parallelReduction(float* input, float* output, int n) {
    extern __shared__ float sharedData[];

    int tid = threadIdx.x;
    int index = blockIdx.x * blockDim.x + tid;

    // Initialize shared memory with input data
    sharedData[tid] = (index < n) ? input[index] : 0.0f;

    // Perform parallel reduction within a block
    for (int stride = blockDim.x / 2; stride > 0; stride >>= 1) {
        if (tid < stride) {
            sharedData[tid] += sharedData[tid + stride];

    // Write the block's result to global memory
    if (tid == 0) {
        output[blockIdx.x] = sharedData[0];

int main() {
    int n = 1024; // Number of elements in the array

    // Host memory
    float* h_data = new float[n];
    for (int i = 0; i < n; ++i) {
        h_data[i] = static_cast<float>(i);

    // Device memory
    float* d_data;
    float* d_partial_sums;

    cudaMalloc((void**)&d_data, sizeof(float) * n);
    cudaMalloc((void**)&d_partial_sums, sizeof(float) * n);

    // Copy data from host to device
    cudaMemcpy(d_data, h_data, sizeof(float) * n, cudaMemcpyHostToDevice);

    // Define grid and block sizes
    int blockSize = 256;
    int gridSize = std::ceil(static_cast<float>(n) / blockSize);

    // Launch parallel reduction kernel
    parallelReduction<<<gridSize, blockSize, sizeof(float) * blockSize>>>(d_data, d_partial_sums, n);

    // Allocate memory for the final result on the host
    float* h_partial_sums = new float[gridSize];

    // Copy partial sums from device to host
    cudaMemcpy(h_partial_sums, d_partial_sums, sizeof(float) * gridSize, cudaMemcpyDeviceToHost);

    // Perform final reduction on the host
    float sum = 0.0f;
    for (int i = 0; i < gridSize; ++i) {
        sum += h_partial_sums[i];

    // Print the result
    std::cout << "Sum: " << sum << std::endl;

    // Free device memory

    // Free host memory
    delete[] h_data;
    delete[] h_partial_sums;

    return 0;

Compiling and Running the Code

To compile and run the code, follow these steps:

  1. Ensure you have the NVIDIA CUDA Toolkit installed on your system.
  2. Save the code to a file with the .cu extension, e.g., parallel_reduction.cu.
  3. Open a terminal and navigate to the directory containing the code file.
  4. Compile the code using.
    nvcc -o parallel_reduction parallel_reduction.cu
  5. Run the executable

You should see the result, which is the sum of elements in the array, printed to the console.

Significance and Applications

Parallel reduction is significant for the following reasons:

  1. Speed: It dramatically accelerates summation and other associative operations on large datasets.
  2. Scalability: Parallel reduction scales efficiently with increasing dataset sizes, making it suitable for big data applications.
  3. GPU Utilization: It fully leverages the parallel processing capabilities of GPUs, ideal for computationally intensive tasks.

Applications of parallel reduction span various domains:

  • Scientific Computing: Used for data analysis in physics, chemistry, and engineering simulations.
  • Financial Modelling: Beneficial for calculating portfolio values or risk assessments on extensive datasets.
  • Data Analytics: Speeds up operations like mean, median, or standard deviation calculations in data analysis.
  • Image and Signal Processing: Efficiently processes large images or signals.
  • Machine Learning: Applied in algorithms like gradient descent for training deep neural networks.


Parallel reduction is a fundamental CUDA technique, enabling the utilisation of GPU parallelism for efficient data summation. Understanding and implementing parallel reduction is invaluable for enhancing the performance of applications across various domains, from scientific simulations to data analysis. By following the steps provided, you can compile and run the code to witness firsthand the power of parallel reduction in action.

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