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Chapter 1: Basics of CUDA Programming Syntax

1. Including Headers

In C++ (and CUDA), we use headers to include predefined functions and classes. The most common header is <iostream>, which provides input/output functionality. For example:

#include <iostream>

2. Data Types

a. Integers (int, short, long)

  • int: Represents whole numbers (e.g., 5, -10, 0).
  • short: Short integers with smaller range.
  • long: Long integers with larger range.

b. Floating-Point Numbers (float, double)

  • float: Single-precision floating-point (e.g., 3.14, -0.001).
  • double: Double-precision floating-point (more accurate, but uses more memory).

c. Other Types

  • char: Represents individual characters (e.g., 'A', 'b', '$').
  • bool: Represents true or false values.

3. Constants

  • Use const to define constants (values that don't change during program execution).

  • Example:

    const float PI = 3.14159;

4. Loops

a. for Loop

  • Repeats a block of code a specified number of times.

  • Example:

    for (int i = 0; i < 10; ++i) {
    // Code to execute
}

b. while Loop

  • Repeats a block of code while a condition is true.

  • Example:

    int count = 0;
while (count < 5) {
    // Code to execute
    ++count;
}

5. The int main() Function

  • Every C++ program starts with the main function.
  • It's the entry point of your program, where execution begins.
  • The int before main indicates that the function returns an integer (usually 0 for successful execution).

6. Writing a Simple Kernel

  • A kernel is a function that runs on the GPU.
  • It's the heart of CUDA programming.
  • Kernels are defined using the __global__ keyword.

Example of a simple kernel:

__global__ void vectorAdd(float* A, float* B, float* C, int size) {
    int tid = blockIdx.x * blockDim.x + threadIdx.x;
    if (tid < size) {
        C[tid] = A[tid] + B[tid];
    }
}

7. Thread Indexing

  • In CUDA, we work with threads.
  • Each thread has a unique index.
  • We calculate the thread index using blockIdx.x, blockDim.x, and threadIdx.x.

8. Memory Allocation

  • We allocate memory for data on both the CPU (host) and GPU (device).
  • cudaMalloc allocates memory on the device.
  • cudaMemcpy transfers data between host and device.

9. Launching the Kernel

  • We launch the kernel using <<<numBlocks, threadsPerBlock>>> syntax.
  • numBlocks and threadsPerBlock determine the grid and block dimensions.

10. Clean Up

  • After using GPU memory, we free it using cudaFree.
  • Also, delete any host memory allocated with new.