CUDA (Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) model created by NVIDIA. It allows software developers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing. This tutorial series will guide you through the basics of CUDA programming.
Getting Started
Before diving into CUDA programming, it's important to have a basic understanding of C/C++ programming and some familiarity with parallel computing concepts.
CUDA Programming Concepts
Here are some key concepts in CUDA programming:
- CUDA Kernels: These are the functions that run on the GPU. They are written in a dialect of C/C++ called CUDA C/C++.
- Memory Hierarchy: CUDA uses a memory hierarchy that includes global, constant, shared, and local memory.
- Thread Hierarchy: CUDA applications are composed of threads that are organized into grids and blocks.
Tutorials
1. CUDA Environment Setup
This tutorial covers how to set up a CUDA development environment on your machine.
# CUDA Environment Setup
To get started with CUDA programming, you need to set up a development environment. This tutorial will guide you through the process of installing CUDA Toolkit and necessary drivers on your system.
[Install CUDA Toolkit](/en/tech/ai/tutorials/cuda-installation)
2. Writing Your First CUDA Kernel
In this tutorial, we'll write a simple CUDA kernel that performs a basic operation on an array.
# Writing Your First CUDA Kernel
In this tutorial, you will learn how to write a CUDA kernel and execute it on the GPU. We'll start with a simple example that adds two arrays together.
[Write Your First CUDA Kernel](/en/tech/ai/tutorials/cuda-kernel)
3. Memory Management in CUDA
This tutorial covers the different types of memory available in CUDA and how to manage them efficiently.
# Memory Management in CUDA
CUDA provides various types of memory to store data. This tutorial explains the different memory types and how to use them effectively in your CUDA programs.
[Memory Management in CUDA](/en/tech/ai/tutorials/cuda-memory)
Conclusion
CUDA programming can be a powerful tool for accelerating computationally intensive tasks. By following these tutorials, you'll gain a solid foundation in CUDA programming and be able to leverage the power of GPUs for your applications.