Day 29: Texture Memory (Practical)

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1743 字

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11 分钟

In this lesson, we implement an image processing kernel using CUDA texture memory. We will create a simple grayscale conversion kernel that reads an input image from texture memory and produces a grayscale output. Texture memory is optimized for spatially coherent (2D) data and provides built-in caching and addressing modes, which are particularly beneficial for image processing applications.

Key Pitfall:
Incorrect normalized coordinates can lead to invalid texture fetches. Ensure that if you use normalized coordinates, you supply values in the range [0, 1]. In this example, we use unnormalized coordinates.

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Table of Contents

1. Overview
2. Understanding Texture Memory
3. Practical Exercise: Grayscale Image Conversion Using Textures
   3.1. Kernel Code: Grayscale Conversion
   3.2. Host Code: Setting Up Texture Memory and Launching Kernel
4. Common Debugging Pitfalls
5. Conceptual Diagrams
6. References & Further Reading
7. Conclusion
8. Next Steps

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1. Overview

Texture memory is a read-only memory space in CUDA that is optimized for 2D data access patterns. It offers:

• Hardware caching for faster fetches.
• Built-in addressing modes (e.g., clamp, wrap) that simplify image boundary handling.
• Interpolation capabilities (e.g., linear filtering) if needed.

In this project, we implement a kernel that converts a color image to grayscale using texture memory. The pipeline includes:

• Allocating a CUDA array to hold the image.
• Copying image data from the host to the CUDA array.
• Binding the CUDA array to a texture reference.
• Launching a kernel that uses tex2D() to sample the image.
• Unbinding the texture and copying the output back to the host.

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2. Understanding Texture Memory

Texture memory in CUDA is ideal for applications where data is accessed in a spatially coherent manner. It is cached, which reduces latency when multiple threads access nearby data locations. When using texture memory:

• Texture coordinates:
  Can be normalized (range [0, 1]) or unnormalized (actual pixel indices).
  In our example, we use unnormalized coordinates by setting normalized = false.
• Binding:
  You bind a CUDA array to a texture reference using cudaBindTextureToArray().
• Fetching:
  The kernel accesses the texture using tex2D(), which returns the value at the specified coordinates.

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3. Practical Exercise: Grayscale Image Conversion Using Textures

We will implement a grayscale conversion kernel. For simplicity, assume the input image is stored as a single-channel (grayscale) image, and the kernel simply reads the pixel value from texture memory and writes it to an output array (or performs minimal processing).

3.1. Kernel Code: Grayscale Conversion

// grayscaleKernel.cu
#include <cuda_runtime.h>
#include <stdio.h>

// Declare a texture reference for 2D texture sampling.
// We use 'cudaTextureType2D' with 'cudaReadModeElementType' to fetch float elements.
texture<float, cudaTextureType2D, cudaReadModeElementType> texRef;

// Kernel for grayscale conversion using texture memory.
// Each thread reads a pixel from texture memory and writes it to the output array.
__global__ void grayscaleKernel(float *output, int width, int height) {
    // Calculate pixel coordinates (x, y).
    int x = blockIdx.x * blockDim.x + threadIdx.x;
    int y = blockIdx.y * blockDim.y + threadIdx.y;

    // Ensure coordinates are within the image boundaries.
    if (x < width && y < height) {
        // When using unnormalized coordinates, tex2D expects pixel indices plus an offset of 0.5.
        float pixel = tex2D(texRef, x + 0.5f, y + 0.5f);
        // For a grayscale image, simply copy the pixel value.
        output[y * width + x] = pixel;
    }
}

Detailed Comments:

• Texture Declaration:
  The texture reference texRef is declared as a global variable.
• Kernel Function:
  Each thread calculates its (x, y) coordinate. A boundary check ensures that only valid pixels are processed.
• Texture Sampling:
  tex2D() is used to sample the image from texture memory. We add 0.5f to the coordinates for proper center sampling in unnormalized coordinates.
• Output:
  The fetched pixel value is stored in a linear output array.

3.2. Host Code: Setting Up Texture Memory and Launching Kernel

// grayscalePipeline.cu
#include <cuda_runtime.h>
#include <stdio.h>
#include <stdlib.h>
#include <time.h>

// Kernel declaration.
__global__ void grayscaleKernel(float *output, int width, int height);

// Declare texture reference (global).
texture<float, cudaTextureType2D, cudaReadModeElementType> texRef;

#define CUDA_CHECK(call) {                                      \
    cudaError_t err = call;                                     \
    if(err != cudaSuccess) {                                    \
        printf("CUDA Error at %s:%d - %s\n", __FILE__, __LINE__, \
               cudaGetErrorString(err));                        \
        exit(EXIT_FAILURE);                                     \
    }                                                           \
}

int main() {
    // Image dimensions.
    int width = 1024, height = 768;
    size_t imgSize = width * height * sizeof(float);

    // Allocate host memory for the image.
    // For demonstration, we simulate a grayscale image.
    float *h_image = (float*)malloc(imgSize);
    if (!h_image) {
        printf("Failed to allocate host memory for image.\n");
        exit(EXIT_FAILURE);
    }

    // Initialize the image with random grayscale values.
    srand(time(NULL));
    for (int i = 0; i < width * height; i++) {
        h_image[i] = (float)(rand() % 256) / 255.0f;
    }

    // Allocate host memory for output image.
    float *h_output = (float*)malloc(imgSize);
    if (!h_output) {
        printf("Failed to allocate host memory for output.\n");
        free(h_image);
        exit(EXIT_FAILURE);
    }

    // Allocate a CUDA array for the image.
    cudaChannelFormatDesc channelDesc = cudaCreateChannelDesc<float>();
    cudaArray_t cuArray;
    CUDA_CHECK(cudaMallocArray(&cuArray, &channelDesc, width, height));

    // Copy image data from host to the CUDA array.
    CUDA_CHECK(cudaMemcpy2DToArray(cuArray, 0, 0, h_image, width * sizeof(float),
                                   width * sizeof(float), height, cudaMemcpyHostToDevice));

    // Set texture parameters.
    texRef.addressMode[0] = cudaAddressModeClamp;   // Clamp x coordinates.
    texRef.addressMode[1] = cudaAddressModeClamp;   // Clamp y coordinates.
    texRef.filterMode = cudaFilterModePoint;        // Use point sampling (no interpolation).
    texRef.normalized = false;                      // Use unnormalized coordinates.

    // Bind the CUDA array to the texture reference.
    CUDA_CHECK(cudaBindTextureToArray(texRef, cuArray, channelDesc));

    // Allocate device memory for the output image.
    float *d_output;
    CUDA_CHECK(cudaMalloc((void**)&d_output, imgSize));

    // Define kernel launch parameters.
    dim3 threadsPerBlock(16, 16);
    dim3 blocksPerGrid((width + threadsPerBlock.x - 1) / threadsPerBlock.x,
                       (height + threadsPerBlock.y - 1) / threadsPerBlock.y);

    // Launch the grayscale conversion kernel.
    grayscaleKernel<<<blocksPerGrid, threadsPerBlock>>>(d_output, width, height);
    CUDA_CHECK(cudaDeviceSynchronize());

    // Copy the output image from device to host.
    CUDA_CHECK(cudaMemcpy(h_output, d_output, imgSize, cudaMemcpyDeviceToHost));

    // Verify output: Print the first 10 pixel values.
    printf("First 10 pixel values of the processed image:\n");
    for (int i = 0; i < 10; i++) {
        printf("%f ", h_output[i]);
    }
    printf("\n");

    // Cleanup: Unbind texture and free all resources.
    CUDA_CHECK(cudaUnbindTexture(texRef));
    CUDA_CHECK(cudaFreeArray(cuArray));
    CUDA_CHECK(cudaFree(d_output));
    free(h_image);
    free(h_output);

    return 0;
}

Detailed Comments:

• Image Preparation:
  The host simulates a grayscale image by allocating and initializing an array of pixel values.
• CUDA Array Allocation:
  A CUDA array is allocated with cudaMallocArray() using a channel descriptor suitable for floats.
• Data Transfer:
  The host image is copied to the CUDA array using cudaMemcpy2DToArray().
• Texture Binding:
  The texture parameters are set (clamping, point sampling, unnormalized coordinates) and the CUDA array is bound to the texture reference.
• Kernel Launch:
  The grayscale conversion kernel is launched with a 2D grid of threads, which samples the texture using tex2D().
• Output Verification:
  The processed image is copied back to host memory and the first 10 pixel values are printed.
• Cleanup:
  The texture is unbound and all allocated resources (CUDA array, device memory, host memory) are freed.

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4. Common Debugging Pitfalls

Pitfall	Solution
Using normalized coordinates when not intended	Set texRef.normalized = false if you want to use pixel indices directly.
Incorrect texture parameter settings (e.g., filter mode)	Ensure you choose the correct filter mode (point vs. linear) and address modes (clamp, wrap).
Failing to bind or unbind the texture correctly	Always bind the CUDA array to the texture reference before kernel launch and unbind it after kernel execution using cudaUnbindTexture().
Not synchronizing after kernel execution	Use cudaDeviceSynchronize() to ensure kernel execution is complete before copying results back.
Memory copy errors due to incorrect pitch or dimensions	Double-check the parameters provided to cudaMemcpy2DToArray().

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5. Conceptual Diagrams

Diagram 1: Texture Memory Workflow for Grayscale Conversion

flowchart TD
    A[Host: Allocate and Initialize Grayscale Image]
    B[Host: Allocate CUDA Array (cudaMallocArray)]
    C[Copy Image Data to CUDA Array (cudaMemcpy2DToArray)]
    D[Set Texture Parameters (Clamp, Point Sampling, Unnormalized)]
    E[Bind CUDA Array to Texture Reference (cudaBindTextureToArray)]
    F[Kernel Launch: Each Thread Computes Pixel Coordinates]
    G[Kernel: Sample Pixel using tex2D() from Texture Memory]
    H[Kernel: Write Pixel Value to Output Array]
    I[Host: Copy Output from Device to Host]
    J[Unbind Texture (cudaUnbindTexture) and Cleanup]
    
    A --> B
    B --> C
    C --> D
    D --> E
    E --> F
    F --> G
    G --> H
    H --> I
    I --> J

Explanation:

• The diagram details the entire workflow: from preparing image data, copying it to a CUDA array, binding it to a texture, launching the kernel for grayscale conversion, and finally unbinding the texture and cleaning up.

Diagram 2: Kernel Execution Flow for Grayscale Conversion

flowchart TD
    A[Kernel Launch]
    B[Each Thread Computes (x, y) Coordinates]
    C[Convert (x, y) to Texture Coordinates (add 0.5)]
    D[Sample Pixel Value using tex2D(texRef, x+0.5, y+0.5)]
    E[Store Sampled Pixel in Output Array]
    
    A --> B
    B --> C
    C --> D
    D --> E

Explanation:

• This diagram shows how each thread in the kernel computes its coordinates, samples the texture using tex2D(), and writes the output.

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6. References & Further Reading

1. CUDA C Programming Guide – Texture Memory
   CUDA Texture Memory Documentation

2. CUDA C Best Practices Guide – Texture Memory
   CUDA Best Practices: Texture Memory

3. NVIDIA CUDA Samples – Texture Memory
   NVIDIA CUDA Samples

4. NVIDIA NSight Compute Documentation
   NVIDIA NSight Compute

5. "Programming Massively Parallel Processors: A Hands-on Approach" by David B. Kirk and Wen-mei W. Hwu
   (Note: This is a book, not a direct linkable resource.)

6. NVIDIA Developer Blog
   NVIDIA Developer Blog

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7. Conclusion

In Day 29, we have:

• Introduced texture memory and its benefits for 2D image processing.
• Implemented a grayscale conversion kernel that samples an image using texture memory.
• Set up the host code to allocate a CUDA array, bind it to a texture, and launch the kernel.
• Highlighted common pitfalls such as incorrect normalized coordinates and missing texture binding/unbinding.
• Provided extensive code examples with detailed inline comments and conceptual diagrams.
• This project serves as a foundation for more advanced image processing tasks using CUDA texture memory.

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8. Next Steps

• Extend the Pipeline:
  Implement additional image processing filters (e.g., Sobel edge detection, Gaussian blur) using texture memory.
• Integrate with Surface Memory:
  Experiment with using surface memory for read-write operations alongside texture memory.
• Profile and Optimize:
  Use NVIDIA NSight Compute to profile texture fetch performance and optimize parameters (filter mode, address mode).
• Real-World Application:
  Integrate this approach into a real-time image processing application.
• Happy CUDA coding, and continue exploring the power of texture memory for high-performance image processing!

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