Learn CUDA with Google Colab: First speedup in 90 minutes

Name: Learn CUDA with Google Colab: First speedup in 90 minutes
Rating: 4.5 (22 reviews)

CUDA. NVIDIA GPU computing stack, Parallel Programming, Google Colab, HPC, CPU vs GPU Performance comparison

Highest Rated

Created byHamza Bashir

Last updated 12/2025

English

What you'll learn

Setup and Verify a GPU Programming environment using Google Colab
Explore CUDA Programming model
Configure threads, blocks and grids correctly to perform operations like vector addition
Calculate thread indices in 1‑D and 2‑D
Write, compile and launch basic CUDA kernels in C/C++
Benchmark and analyse performance – measure CPU vs. GPU execution time

Course content

4 sections • 11 lectures • 1h 23m total length

Introduction1:50
Get started with GPU programming the easy way.
In this intro, I will walk you through the course structure, learning goals, and what makes this course unique
Why GPU needed3:58
In this lesson, you’ll learn why GPUs are the powerhouses behind modern AI, graphics, and scientific computing. We’ll break down the architectural differences between CPUs and GPUs — from how their cores are designed to how they execute instructions. You’ll discover why GPUs excel at parallel workloads and when (and when not) to use them.
Through visual diagrams and relatable analogies, we’ll make complex hardware concepts intuitive and memorable.
Integrated vs Dedicated GPU4:23
In this lecture, we’ll explore one of the most common questions in computer hardware and GPU computing — what’s the difference between integrated and dedicated GPUs? We’ll also dive into the major industry players: Compare NVIDIA vs Intel/AMD options, By the end of this lecture, you’ll clearly understand the trade-offs between performance, cost, power efficiency, and upgradability — helping you choose the right GPU setup for your needs, whether it’s a lightweight laptop or a powerful workstation
What is CUDA, NVIDIA GPU computing stack3:11
In this lecture, we explore the foundation of NVIDIA’s GPU computing platform — CUDA. We’ll break down the CUDA programming model. You’ll also discover the NVIDIA GPU computing stack, broader NVIDIA ecosystem, and how CUDA integrates with popular programming languages and AI frameworks.
Section 1 Quiz

Compute Capability7:56
You will learn how to verify if your GPU supports CUDA by checking its compute capability in Google Colab. You’ll enable GPU runtime, run !nvidia-smi, check CUDA availability in PyTorch, and match specs on NVIDIA’s site.
CUDA Programmming Model5:06
In this lecture, you’ll learn the fundamentals of the CUDA programming model — how Host (CPU) and Device (GPU) work together to accelerate computation.
We’ll break down how kernels are written and launched using CUDA syntax, and how threads, blocks, and grids enable massive parallelism.
You’ll also see the complete CUDA workflow — from memory allocation and data transfer to kernel execution and synchronization — giving you a strong foundation to start writing real GPU code.
Hello World11:08
In this hands-on lecture, you’ll set up Google Colab for CUDA development and write your first GPU program
Learn how to use NVCC to compile and run CUDA code directly in Colab. and see real GPU computation in action
Section 2 Mid Test
Thread, Blocks, Grids16:05
In this lecture, you’ll learn how CUDA organizes parallel execution using threads, blocks, and grids.
Understand how threads work as individual execution units, how blocks group threads for shared memory and synchronization, and how grids enable massive parallelism.
You’ll also see practical examples of thread indexing, global thread IDs, and kernel launches — giving you the foundation to scale your CUDA programs efficiently across thousands of threads.
Thread Indexing12:53
In this lecture, you’ll learn how to map GPU threads efficiently to your data using 1D and 2D indexing.
Understand how threadIdx, blockIdx, and blockDim define each thread’s unique role in vector and matrix operations.
Through practical CUDA code examples, you’ll see how to compute global thread IDs for both linear and 2D data layouts — a key skill for writing scalable, high-performance GPU kernels.

CPU vs GPU Performance Comparison16:39
In this lecture, you’ll learn how to benchmark and compare CPU vs GPU performance using a simple vector addition example. You’ll write both CPU and CUDA versions, measure execution time using chrono and CUDA Events, and visualize how GPU parallelism delivers massive speedups for large data sizes. By the end, you’ll understand why GPUs outperform CPUs in parallel workloads and how to run your own CUDA performance experiments.

Requirements

Basic understanding of C/C++ programming

Description

This course takes you on a practical journey into GPU-accelerated computing using NVIDIA CUDA — the most widely used platform for parallel programming. Whether you’re a student, engineer, or developer, you’ll learn how to harness thousands of GPU cores to achieve performance levels far beyond what CPUs can offer.

Starting from the fundamentals of GPU architecture, you’ll gradually move into hands-on CUDA programming — understanding threads, blocks, grids, and how to map computations efficiently across GPU hardware

What You’ll Learn

Why GPUs are essential for high-performance computing
Difference between Integrated vs. Dedicated GPUs
What CUDA is and how it enables parallel processing
The NVIDIA GPU computing stack explained — hardware to software
Understanding Compute Capability and how it affects performance
The CUDA programming model: Host vs. Device execution
Writing your first CUDA program: Hello World
Deep dive into Threads, Blocks, and Grids
Thread indexing for efficient parallel computation
CPU vs GPU performance comparison through practical examples
Quizzes to reinforce key concepts at every stage

Why Take This Course?

Taught by an expert with real-world experience in GPU-based signal processing and AI
Combines theory with hands-on CUDA coding examples
Learn to think in parallel and optimize your algorithms for performance
Prepare yourself for a career in AI, scientific computing, data processing, or graphics programming

Who this course is for:

This course is for you even you don’t have access to (or don’t want to configure) a dedicated GPU
You’re curious about the CUDA ecosystem and want practical Colab notebooks to experiment with kernels, threads/blocks, and performance analysis.
You are an embedded engineer and extend your expertise in GPU programming
You know basic C/C++ and wants to accelerate compute‑intensive tasks (e.g. machine learning, signal or image processing, scientific computing)
You’re looking for a concise, beginner‑friendly course that takes you from “Why GPUs?” through memory hierarchies and synchronization concepts, with real CPU‑vs‑GPU benchmarks

Learn CUDA with Google Colab: First speedup in 90 minutes

What you'll learn

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Course content

Introduction4 lectures • 13min

Hello World5 lectures • 53min

Your First CUDA Benchmark — CPU vs GPU1 lecture • 17min

Bonus Lecture: Thank You & Next Steps1 lecture • 1min

Requirements

Description

Who this course is for: