Learn CUDA with Google Colab: First speedup in 90 minutes

Learn CUDA with Google Colab: First speedup in 90 minutes
Published 11/2025
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch

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

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

Requirements
Basic understanding of C/C++ programming

Description
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