Ready to unlock the power of GPU programming?
In this beginner-friendly tutorial, you’ll learn how to set up a C/C++ development environment in VS Code, install the NVIDIA CUDA Toolkit, and write your first CUDA program—a parallel vector addition kernel.
Prerequisite:
- CUDA Installed
- Python installed
🌟 What You’ll Learn
✔ What CUDA is and why it matters in modern computing
✔ Setting up C/C++ development in Visual Studio Code
✔ Verifying your GPU + compiler environment
✔ Writing and running your first CUDA program
✔ Implementing parallel vector addition using CUDA kernels
✔ Compiling CUDA code with nvcc
🧠 Who Is This For?
Beginners who want to get started with:
CUDA programming
High-performance GPU computing
AI/ML accelerators
Parallel programming with GPUs
Low-level performance engineering
🛠 Tools Used
NVIDIA CUDA Toolkit
VS Code (with C/C++ extensions)
GCC/Clang
NVCC compiler
⏱ Timestamps
(You can update these after upload)
00:00 – What and Why CUDA programming?
00:45 – Setting up C/C++ in VS Code
02:40 – Writing your first CUDA program
04:30 – CUDA Kernel Function
09:00 – Standard C/C++ Function
21:15 – Compiling Cuda Program with NVCC
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Whether you're starting your journey into high-performance computing, AI acceleration, or GPU-optimized ML systems, this video will guide you step by step.
