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CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model developed by NVIDIA. It allows developers to harness the computational power of NVIDIA GPUs (Graphics Processing Units) to accelerate general-purpose computing tasks. In this tutorial, we will explore the basics of CUDA programming through examples, highlighting key concepts and providing code snippets.
Before you begin, ensure that you have the following:
NVIDIA GPU: Make sure your system has an NVIDIA GPU that supports CUDA. You can check the official NVIDIA CUDA GPU list for compatibility.
CUDA Toolkit: Download and install the CUDA Toolkit from the official NVIDIA website (https://developer.nvidia.com/cuda-downloads).
CUDA-capable compiler: Use a compiler compatible with CUDA, such as NVIDIA's NVCC (NVIDIA CUDA Compiler).
Let's start with a simple "Hello CUDA" program. Create a file named hello_cuda.cu with the following code:
Compile the code using the following command:
Run the executable:
This program defines a CUDA kernel (helloCUDA) that is executed on the GPU. The CPU and GPU print messages, demonstrating concurrent execution.
Now, let's explore a basic vector addition example. Create a file named vector_addition.cu with the following code:
Compile and run the code as before:
This example demonstrates the basic structure of a CUDA program, including memory allocation, data transfer, kernel launch, and result retrieval.
These two examples provide a foundation for understanding CUDA programming. Further exploration can include topics like shared memory, thread synchronization, and optimization techniques to leverage the full potential of GPU parallelism. The official CUDA documentation and resources are valuable references for deepening your understanding of CUDA programming.
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