Alex Xi's Notes

host preparation

Jul 13, 20259 min read

GPU Study Group Host Preparation Guide

Getting Ready for Session 1: GPU Architecture & CUDA Setup

📅 Timeline: 1 Week Before Session

Day 7: Initial Technical Setup & Content Review

✅ Master the Technical Content Yourself

# 1. Test your CUDA environment thoroughly
nvcc --version
nvidia-smi
nvidia-smi -L
 
# 2. Run deviceQuery sample
find /usr -name "deviceQuery" 2>/dev/null
# If found, compile and run it
 
# 3. Test all exercise code
# Compile and run hello_cuda.cu with different configurations
nvcc hello_cuda.cu -o hello_cuda
./hello_cuda
 
# Try variations: <<<3,2>>>, <<<1,8>>>, <<<1,1024>>>

✅ Complete All Required Readings

  • NVIDIA CUDA Programming Guide - Chapter 1 (take detailed notes)
  • An Even Easier Introduction to CUDA (bookmark key concepts)
  • FreeCodeCamp CUDA Course - First 2 hours (note timestamps for demos)
  • GPU Architecture Basics video

Pro tip: Take notes on potential questions participants might ask

✅ Practice Key Explanations

Practice explaining these concepts out loud (seriously!):

  • GPU vs CPU architecture - Use analogies (highway with many lanes vs one fast lane)
  • SIMT execution model - “Same instruction, multiple threads”
  • Thread hierarchy - Draw it out on paper
  • Host/Device concept - “Two different computers talking to each other”

📅 Day 5-6: Content Mastery & Backup Preparation

✅ Deep Dive Into Discussion Topics

Discussion 1: “Why do GPUs excel at parallel tasks?”

Your talking points:

  • CPUs: 4-16 complex cores, optimized for single-thread performance
  • GPUs: Thousands of simple cores, optimized for throughput
  • Analogy: CPU = Formula 1 car (fast, few), GPU = bus fleet (many, coordinated)
  • Memory bandwidth vs latency trade-offs

Practice questions to ask:

  • “What happens when a CPU hits a cache miss?”
  • “How would you multiply two 1000x1000 matrices on CPU vs GPU?”
  • “When would you NOT want to use a GPU?”

Discussion 2: “How does SIMT impact algorithm design?”

Your talking points:

  • All threads in a warp (32 threads) execute the same instruction
  • Branch divergence causes performance penalties
  • Need to think in data parallelism, not task parallelism

Real examples to mention:

  • ✅ Matrix multiplication: Perfect fit (same operation on many elements)
  • ⚠️ Sorting: Needs algorithmic changes (merge sort → bitonic sort)
  • ❌ Tree traversal: Highly irregular, poor GPU fit

Discussion 3: “What development challenges do you anticipate?”

Expected responses & your follow-ups:

  • “Debugging parallel code” → “We’ll learn tools like cuda-gdb and printf debugging”
  • “Memory management” → “Week 2 is all about this!”
  • “Performance optimization” → “Weeks 8-10 focus on optimization”

✅ Set Up Backup Solutions

Google Colab Backup

  1. Create new notebook at colab.research.google.com
  2. Test GPU runtime: Runtime → Change runtime type → GPU
  3. Test basic commands:
!nvidia-smi
!nvcc --version
  1. Create cells for each exercise with %%writefile magic

Alternative Platforms

  • Compiler Explorer: Test CUDA code at godbolt.org
  • Replit: CUDA-enabled environment for quick demos
  • Your laptop: Ensure you can share screen clearly

📅 Day 3-4: Practice & Logistics

✅ Run a Practice Session

Do this with a friend, family member, or even alone:

  1. Time yourself going through each section
  2. Practice transitions between slides
  3. Test your screen sharing and presentation setup
  4. Practice explaining code line by line
  5. Practice facilitating discussion - ask questions, pause for responses

Common timing issues:

  • Discussion points often run long → Set gentle timers
  • Technical issues eat time → Have backups ready
  • Exercises vary by skill level → Prepare extensions and simplifications

✅ Logistics Preparation

Room/Virtual Setup

  • Test screen sharing with slides and terminal
  • Check audio/video quality
  • Prepare shared document for collaborative notes
  • Test any whiteboard/drawing tools you plan to use

Materials Organization

  • Bookmark all slides and reference materials
  • Print quick reference card (or have it open on second screen)
  • Prepare exercise files in easily accessible folder
  • Test compilation of all code examples

Communication Prep

  • Draft reminder email (see template below)
  • Prepare participant list with contact info
  • Set up feedback collection method

📅 Day 1-2: Final Preparation & Messaging

✅ Send Participant Reminder

Email Template:

Subject: GPU Study Group Session 1 Tomorrow - Final Prep

Hi everyone!

Looking forward to our first GPU study group session tomorrow! Quick reminders:

📅 When: [Date/Time]
🔗 Where: [Meeting link]
⏱️ Duration: 90 minutes

🛠️ Technical Prep (if you haven't already):
- Install CUDA Toolkit: https://developer.nvidia.com/cuda-downloads
- Test with: nvcc --version && nvidia-smi
- Backup: We'll have Google Colab ready for anyone with issues

📖 Reading Refresh:
If you want a quick review, focus on:
- NVIDIA CUDA Programming Guide Ch. 1 (terminology)
- GPU vs CPU architecture concepts

🎯 What to Expect:
- 15 min: Discussion on GPU architecture
- 30 min: Hands-on CUDA exercises
- Interactive throughout - bring questions!

Technical issues? Reply to this email and I'll help troubleshoot.

See you tomorrow!
[Your name]

✅ Final Technical Check

  • Re-run all exercises one more time
  • Test your internet connection and backup
  • Charge devices and check power adapters
  • Close unnecessary applications to ensure smooth screen sharing

✅ Mental Preparation

  • Review your speaker notes from the slides
  • Prepare for common questions (see FAQ section below)
  • Set intentions: Focus on learning together, not being perfect
  • Plan energy management: Coffee/water, bathroom breaks

📅 Day of Session: Pre-Meeting (1 Hour Before)

✅ Technical Setup Checklist

  • Join meeting room early (15-30 minutes)
  • Test screen sharing with slides and terminal
  • Open all necessary applications:
    • Slide presentation
    • Terminal/command prompt
    • Text editor with exercise files
    • Shared document for notes
    • Quick reference card
  • Test audio/video one final time

✅ Content Review (10 minutes)

  • Skim slide speaker notes for timing reminders
  • Review discussion questions and expected responses
  • Check exercise code is ready to copy/paste
  • Prepare backup solutions (Colab, shared screen)

✅ Mindset Preparation

  • Remember: You’re facilitating learning, not lecturing
  • Expect questions - they’re signs of engagement!
  • Technical issues are normal - stay calm and pivot to backups
  • Energy matters - be enthusiastic about GPU programming!

🔥 Pro Tips for Success

Facilitation Best Practices

  1. Start with energy - Set excited, curious tone
  2. Use names early - Learn and use participant names
  3. Embrace the “I don’t know” - “Great question! Let’s figure it out together”
  4. Encourage questions - “There are no stupid questions in GPU programming”
  5. Celebrate small wins - Every successful nvcc compilation matters!

Technical Teaching Tips

  1. Narrate while coding - “Now I’m opening the terminal…”
  2. Make mistakes intentionally - Show debugging process
  3. Use analogies heavily - Complex concepts need simple comparisons
  4. Code together - Don’t just show, do it together
  5. Connect to real world - “Netflix uses this for recommendations…”

Managing Different Skill Levels

  1. Pair beginners with experienced participants
  2. Prepare extension exercises for quick learners
  3. Focus on concepts over syntax for struggling participants
  4. Use “parking lot” for advanced questions

Time Management

  1. Set gentle timers for discussions (use phone)
  2. Have transition phrases ready: “Let’s dive into the exercises…”
  3. Know what to cut if running long (reduce exercise variations)
  4. Build in buffer time for technical issues

🆘 Common Questions & Your Responses

”I can’t install CUDA / My installation isn’t working”

Your response: “No problem! Let’s get you set up on Google Colab right now. Everyone else, keep going with the local setup. [Share Colab link]. This is why we have backup plans!"

"What if I don’t have an NVIDIA GPU?”

Your response: “You can still learn all the concepts! We’ll use cloud resources like Colab, and understanding GPU programming principles applies to all parallel computing."

"This seems really complicated compared to regular programming”

Your response: “You’re absolutely right - it is different! That’s exactly why we’re spending 12 weeks on it. By the end, you’ll have a solid mental model that makes it feel natural."

"How is this different from OpenCL/other GPU programming?”

Your response: “Great question! CUDA is NVIDIA-specific but widely used. The concepts we learn apply broadly. We might do a comparison session later in the program."

"When would I actually use this in my job?”

Your response: “Anywhere you have embarrassingly parallel problems: data processing, machine learning, scientific computing, cryptocurrency, image processing, simulations…”

📊 Success Metrics

Green Flags (Session Going Well)

  • Multiple people asking questions
  • Participants helping each other with technical issues
  • Discussion naturally extending concepts
  • Successful exercise completion by most participants
  • Energy and engagement remain high

Yellow Flags (Need Adjustment)

  • One person dominating conversation
  • Multiple technical issues
  • Silence during discussion periods
  • Time running significantly over/under
  • Repeated confusion about basic concepts

Red Flags (Immediate Intervention Needed)

  • More than half participants can’t complete exercises
  • Technical issues overwhelming content
  • Hostility or discouragement
  • Complete silence from group
  • You’re lecturing instead of facilitating

🎯 Post-Session Action Plan

Immediate (Within 2 Hours)

  • Send thank you message to all participants
  • Note what worked well and what needs adjustment
  • Identify participants who need individual technical help
  • Review timing - what sections need more/less time?

Within 24 Hours

  • Send detailed session summary with key takeaways
  • Share exercise solutions and explanations
  • Distribute Week 2 materials and reading list
  • Schedule individual help for struggling participants
  • Update session plan based on today’s experience

Planning Week 2

  • Adjust difficulty based on group’s performance
  • Address gaps from Session 1
  • Prepare more backup plans if technical issues were prominent
  • Consider group dynamics in session structure

🚀 Final Confidence Boosters

Remember:

  • You don’t need to be perfect - you’re learning together
  • Technical issues are part of GPU programming - normalize them
  • Questions mean engagement - celebrate them!
  • Your enthusiasm is contagious - let your excitement show
  • Everyone started as a beginner - create a supportive environment

You’re Ready When:

  • You can explain GPU vs CPU differences clearly
  • You can run all exercises without looking at notes
  • You have backup plans for technical issues
  • You’re excited to share GPU programming with others

Emergency Mantra:

“We’re all learning GPU programming together. Technical issues are normal. Questions are welcome. Let’s figure this out as a team.”

You’ve got this! Your participants are lucky to have such a well-prepared host. 🎉

The fact that you’re taking time to prepare this thoroughly shows you care about creating a great learning experience. Trust your preparation, stay flexible, and remember that the best study groups are collaborative learning environments, not perfect presentations.

Go make some GPU programmers! 🚀

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