How to Run CUDA C/C++ on Jupyter Notebook in Google Colaboratory

Have you ever wanted to explore the world of parallel computing and GPU acceleration? If so, you’ve come to the right place! In this blog post, we’ll guide you through the exciting process of running CUDA C/C++ code on Jupyter Notebook using Google Colaboratory (Colab). We’ll break down the concepts, step-by-step, making it easy to follow even if you’re new to programming.

Introduction

If you’re curious about how to harness the power of NVIDIA GPUs for programming but don’t know where to begin, you’ve come to the right place! In this guide, I’ll walk you through running CUDA C/C++ programs using Jupyter Notebooks in Google Colaboratory (Colab). Even if you’ve never heard of CUDA or don’t know how to program, don’t worry. I’ll explain everything in simple terms so that by the end, you’ll understand how to get started with GPU programming on Colab.

What Will You Learn?

In this article, we will cover the following topics:

• What is CUDA, and why do we use it?
• Understanding Google Colab and Jupyter Notebooks.
• Step-by-step instructions to run CUDA C/C++ on Google Colab.
• Frequently asked questions (FAQs).

By the end of this guide, you’ll know how to execute GPU-accelerated code using the cloud!