Lesson Plan:
GPU Programming Techniques in Hybrid MIMD Systems Class Duration: 52 Minutes
Topic:
Students will analyze and implement GPU programming techniques in hybrid MIMD systems to
evaluate performance metrics such as speedup, efficiency, and scalability, while applying
Amdahl’s law and conducting timing measurements on MIMD programs to optimize GPU
performance.
1. Gain Learners' Attention (5 minutes)
Activity 1: Show a short video clip demonstrating the dramatic performance
improvements in GPU programming compared to CPU-only solutions.
Activity 2: Present a real-world example where GPU programming significantly
optimizes performance, such as in gaming or scientific simulations.
2. Inform Students of the Learning Objective (3 minutes)
Activity 1: Clearly state the learning objectives on the board:
Analyze GPU programming techniques in MIMD systems.
Evaluate performance metrics: speedup, efficiency, and scalability.
Apply Amdahl’s law and conduct timing measurements.
Activity 2: Discuss how mastering these skills will benefit their future studies and
careers in computer science and engineering.
3. Stimulate Recall of Prior Knowledge (5 minutes)
Activity 1: Conduct a quick quiz on prior knowledge of MIMD systems and basic GPU
concepts.
Activity 2: Facilitate a brief discussion asking students to share any prior experiences
with parallel programming or GPU technologies.
4. Present To-Be-Learned Material (10 minutes)
Activity 1: Provide a lecture on GPU architecture and the principles of hybrid MIMD
systems, including a visual diagram.
Activity 2: Distribute a handout summarizing key concepts of Amdahl’s law and
performance metrics, followed by a brief explanation.
5. Provide Learning Guidance (5 minutes)
Activity 1: Introduce programming tools and libraries (like CUDA or OpenCL) relevant
for GPU programming.
Activity 2: Demonstrate a sample code snippet that illustrates the basic structure of a
GPU program, explaining each part.
6. Facilitate Student Practice (10 minutes)
Activity 1: Group students to work on a simple lab exercise where they implement a
basic GPU program.
Activity 2: Encourage students to modify the sample code provided and observe changes
in performance metrics.
7. Provide Feedback to Students (5 minutes)
Activity 1: Circulate around the room to provide on-the-spot feedback on their code
implementations.
Activity 2: Hold a quick Q&A session where students can ask questions about their
challenges and clarify misunderstandings.
8. Assess Learning (5 minutes)
Activity 1: Conduct a formative assessment through a quick individual reflection or exit
ticket where students must summarize the key performance metrics.
Activity 2: Administer a short multiple-choice quiz that assesses their understanding of
GPU programming concepts and Amdahl’s law.
9. Enhance Retention and Transfer (4 minutes)
Activity 1: Discuss how the learned concepts can be applied in other areas of computer
science and engineering.
Activity 2: Assign a follow-up homework task where students must research a recent
advancement in GPU technology and its impact on a specific field.
