Cross-Platform CUDA C++ Masterclass: GPU Architecture & Block-Thread Management

Dive into the world of CUDA hardware acceleration with this comprehensive guide, perfect for beginners and seasoned developers alike. In this video, we start with the basics of CPU-GPU communication using a simple vector addition example, then progress to advanced concepts in CUDA memory management, multi-dimensional traversal algorithms, and GPU architecture.
We’ll explore how to map 2D and 3D data structures onto 1D memory in GPU, a critical concept for efficient memory usage. You’ll also learn about the 2D and 3D traverse algorithms, essential for working with complex data grids, as well as the internal structure of GPUs, including grids, blocks, and threads. With these fundamentals in place, we’ll code a CUDA matrix transpose kernel from scratch, optimize it using calculated block and thread sizes, and ensure it runs seamlessly on both Windows and Linux platforms.
Finally, we'll cover CMake configuration for multi-platform CUDA development, ensuring your code is set up for maximum performance on any OS. By the end, you’ll have a solid understanding of block and thread calculations, GPU architecture, and how to write optimized CUDA code for real-world applications.
🔍 What You’ll Learn
How to write generic CUDA kernels that work with various data types using templates.
Insight into GPU architecture and memory layout, and how it affects kernel performance.
Step-by-step creation of a multi-dimensional data processing example, moving from 1D to 2D and beyond.
Practical tips on optimizing memory usage and data transfers for CUDA applications.
Core concepts of cross-platform CUDA development, enabling code that runs smoothly on both Windows and Linux.
Whether you're new to CUDA or looking to deepen your understanding of GPU memory management and architecture, this video has something for everyone. Don’t forget to like, subscribe, and hit the bell for more tutorials on CUDA, GPU programming, and cross-platform C++ development!
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