CUDA. NVIDIA GPU computing stack, Parallel Programming, Google Colab, HPC, CPU vs GPU Performance comparison
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.
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
- Introduction –> 4 lectures • 13min.
- Hello World –> 5 lectures • 53min.
- Your First CUDA Benchmark — CPU vs GPU –> 1 lecture • 17min.
- Bonus Lecture: Thank You & Next Steps –> 1 lecture • 1min.
Requirements
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