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
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 hardwareWhat You'll LearnWhy GPUs are essential for high-performance computingDifference between Integrated vs. Dedicated GPUsWhat CUDA is and how it enables parallel processingThe NVIDIA GPU computing stack explained — hardware to softwareUnderstanding Compute Capability and how it affects performanceThe CUDA programming model: Host vs. Device executionWriting your first CUDA program: Hello WorldDeep dive into Threads, Blocks, and GridsThread indexing for efficient parallel computationCPU vs GPU performance comparison through practical examplesQuizzes to reinforce key concepts at every stageWhy Take This Course?Taught by an expert with real-world experience in GPU-based signal processing and AICombines theory with hands-on CUDA coding examplesLearn to think in parallel and optimize your algorithms for performancePrepare 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