GPU Programming: A Comprehensive Four-Quarter Journey

Prerequisites:

• Good understanding of a high-level programming language (preferably C++ or Python).
• Fundamental understanding of computer hardware and architecture.
• Knowledge of essential computer science concepts such as algorithms and data structures.

First Quarter: Introduction to Computer Architecture and Fundamentals of GPU Computing

Goal: Understanding the basics of GPU architecture and writing simple CUDA programs.

1. Learning Computer Architecture Basics - Goal: Understand how computers work

   • Primary Resource: "Introduction to High-Performance and Parallel Computing" on coursera (Link).
   • Alternative Resources: Computer Organization and Architecture: Designing for Performance (Book) and "Computer Systems: A Programmer's Perspective by Randal E. Bryant, David R. O'Hallaron".
   • Supplementary Resource: "Computer Architecture: A Quantitative Approach" by John L. Hennessy and David A. Patterson. This is a milestone book in computer architecture concepts that'll provide an in-depth understanding of the subject.
   • Evaluation: Online quizzes and tests provided by the course.
   • Time Management: Learn at your own pace but try to devote about 10 hours a week.

2. Introduction to GPU Computing - Goal: Write basic CUDA programs

   • Primary Resource: "Introduction to Parallel Programming with CUDA" on Coursera (Link).
   • Alternative Resource: "CUDA by Example: An Introduction to General-Purpose GPU Programming" by Jason Sanders, Edward Kandrot (Link).
   • Hands-on exercises: Try to implement simple programs such as vector addition, matrix multiplication, etc.
   • Evaluation: Mini-projects based on the exercises, and peer review.
   • Time Management: Spend at least two hours a week solving problems.

3. Setting Up Your Development Environment - Goal: Set up a functional CUDA programming environment

   • Software: CUDA Toolkit, VS Code or similar code editor.
   • Practical exercises: Compile and run sample codes provided in the CUDA toolkit.
   • Evaluation: Try implementing basic GPU programs like vector addition or matrix multiplication. This will ensure that you're able to use the development environment properly and understand its workflow.

Second Quarter: Intermediate GPU Programming and Optimization

Goal: Gain deeper understanding of GPU programming and learn to optimize GPU code.

1. Deepening GPU Programming Knowledge - Goal: Understand advanced concepts in GPU programming

   • Primary Resource: Parallel Computer Architecture and Programming course from Carnegie Mellon University (Link).
   • Alternative Resource: Cuda by Example: An Introduction to General-Purpose GPU Programming (Book), "Advanced Computer Architecture" course from University of Illinois on Coursera.
   • Evaluation: Online quizzes and tests provided by the course.
   • Time Management: Learn at your own pace but aim for about 10 hours a week.

2. GPU Optimization - Goal: Learn how to optimize CUDA code

   • Primary Resource: CUDA Performance Optimization guide from Nvidia (Link).
   • Alternative Resource: Programming Massively Parallel Processors: A Hands-on Approach (Book).
   • Supplementary Resource: "CUDA Programming: A Developer's Guide to Parallel Computing with GPUs" by Shane Cook. This book has dedicated sections on GPU optimization which can serve as a valuable resource.
   • Hands-on exercises: Try optimizing the code you've written so far.
   • Evaluation: Compare the performance of optimized code with the original code.
   • Time Management: Spend about 5 hours a week studying this guide.

Third Quarter: Implementing Advanced GPU Programming in Real-life Projects

Goal: Learn about applying GPU programming in deep learning and understand heterogeneous computing.

1. Deep Learning on GPU - Goal: Learn how to run deep learning models on a GPU

   • Primary Resource: Deep Learning on GPU course on Coursera (Link).
   • Alternative Resource: Deep Learning for Coders with Fastai and PyTorch (Book).
   • Hands-on exercises: Implement popular deep learning architectures using GPU. Use frameworks like Tensorflow or PyTorch for this purpose. This will give you practical exposure to using GPU for deep learning.
   • Evaluation: Online quizzes and tests provided by the course.
   • Time Management: Devote about 10 hours a week.

2. Introduction to Heterogeneous Computing - Goal: Implement heterogeneous computing

   • Primary Resource: Heterogeneous Parallel Programming course on Coursera (Link).
   • Alternative Resource: Heterogeneous Computing with OpenCL (Book).
   • Evaluation: Online quizzes and tests provided by the course.
   • Time Management: Spend about 10 hours a week.

3. GPU Projects - Goal: Apply GPU programming skills in real-world scenarios

   • Resource: GitHub projects involving GPU programming.
   • Hands-on exercises: Choose projects that allow you to tap into different aspects of GPU programming. Examples can be image processing tasks, complex numerical computations, or even game development.
   • Peer-to-peer learning: Collaborate with peers on group projects.
   • Evaluation: Project review by AI like ChatGPT.

Fourth Quarter: Specializing in a Domain and Engaging with the GPU Programming Community

Goal: Specialize in a domain within GPU programming and engage with the programming community.

1. Specialization - Goal: Develop expertise in specific applications of GPU programming

   • Resource: Books, tutorials, courses specific to the chosen domain.
   • Hands-on exercises: Implement advanced projects in your chosen specialization, for instance, real-time ray tracing for a gaming specialization, or large scale training of neural networks for a deep learning specialization.
   • Practical exercises: Implement advanced projects related to the chosen domain.
   • Evaluation: Advanced project review by peers and mentors.

2. Exploration of Other GPU Programming Tools - Goal: Understand other GPU programming languages and tools

   • Resource: OpenCL, DirectCompute tutorials, and guides.
   • Alternative Resource: "OpenCL Programming by Example" by Ravishekhar Banger, Koushik Bhattacharyya. This book will give you a good start with OpenCL.
   • Evaluation: Write basic programs using other GPU programming languages.
   • Time Management: Learn at your own pace but aim for about 5 hours a week.

3. Learning Community Engagement - Goal: Connect with the larger GPU programming community

   • Resource: NVIDIA Developer Forums, StackOverflow.
   • Supplementary Resource: Join relevant LinkedIn Groups, Reddit threads, follow leading personalities on Twitter, and participate in GPU programming webinars and meetups. This will help you network with professionals and stay updated about industry developments.
   • Evaluation: Regular participation in discussions, problem-solving.

While time estimates for each activity have been removed to respect individual learning speeds, it's recommended to have a consistent study plan, dedicating a certain number of hours each week to the course. Regular breaks are crucial to maintaining mental health and ensuring effective learning. Additionally, the resources mentioned in this plan are mostly free and available in English. Some may require specific hardware or software; alternatives have been provided for such cases.