Mastering Parallel programming with CUDA platform

Unofficial guide to parallel programming on GPU's with CUDA from basic concepts to advance algorithm implementations.

This course is an in-depth, unofficial guide to parallel programming using GPU computing techniques with C++. We'll begin by exploring foundational concepts such as the GPU programming model, execution structure, and memory hierarchy. From there, you’ll dive into hands-on development, implementing advanced parallel algorithms optimized for high-performance graphics processors.

Since performance is at the heart of GPU-based computing, this course places a strong emphasis on optimization techniques. You’ll learn how to fine-tune your code for maximum speed and efficiency, and apply industry-standard tools for profiling and debugging, including nvprof, nvvp, memcheck, and GDB-based GPU debuggers.

The course includes the following core sections:

• Introduction to GPU programming concepts and execution models

• Understanding execution behavior on parallel processors

• Deep dive into memory systems: global, shared, and constant memory

• Using streams to manage concurrent execution

• Fine-tuning instruction-level behavior for performance

• Implementing real-world algorithms using GPU acceleration

• Profiling and debugging tools overview

To reinforce learning, this course includes programming exercises and quizzes designed to help you internalize each concept.

This is the first course in a masterclass series on GPU-based parallel computing. The knowledge you gain here will form a strong foundation for exploring more advanced topics in future courses.

As GPUs continue to drive innovation in fields like AI and scientific computing, mastering these tools and techniques will set you apart in the tech industry.

Note: This course is not affiliated with or endorsed by NVIDIA Corporation. CUDA is a registered trademark of NVIDIA Corporation, used here solely for educational reference purposes.