GPU Study Group Host: Quick Prep Checklist
Essential tasks to prep for your first session
🔥 Priority 1: Must Do (2-3 hours total)
Technical Mastery
- Test your CUDA setup thoroughly - run
nvcc --version,nvidia-smi, compile hello_cuda.cu - Complete all exercises yourself - understand every line of code you’ll be showing
- Set up Google Colab backup - test GPU runtime, create cells for each exercise
- Practice screen sharing with slides + terminal simultaneously
Content Mastery
- Complete required readings - take notes on concepts you’ll need to explain
- Practice explaining key concepts out loud:
- GPU vs CPU (use analogies!)
- Thread hierarchy (threads → blocks → grid)
- Host/Device concept
- SIMT execution model
Logistics
- Send reminder email to participants 24 hours before
- Test meeting platform and screen sharing
- Organize all materials - slides, code files, quick reference easily accessible
📋 Priority 2: Recommended (1-2 hours)
Session Practice
- Run through slides with timing - aim for 90 minutes total
- Practice facilitating discussions - prepare follow-up questions
- Prepare for common questions (see comprehensive guide)
Backup Preparations
- Identify participants who might need technical help
- Prepare simplified versions of exercises if needed
- Have plan for mixed skill levels (pairing strategy)
⚡ Priority 3: Nice to Have (30 minutes)
Polish
- Print quick reference card for easy access during session
- Create shared document for collaborative notes
- Prepare energy management (coffee, water, break plan)
🎯 Day-Of Quick Setup (30 minutes before)
Technical
- Join meeting 15-30 minutes early
- Test screen sharing with slides + terminal
- Open: slides, terminal, text editor, shared doc, quick reference
- Close unnecessary applications
Mental
- Review speaker notes quickly
- Remember: You’re facilitating, not lecturing
- Set intention: Create collaborative learning environment
- Energy check: Get excited about GPU programming!
🆘 Emergency Quick Reference
If Technical Issues Arise
- Stay calm - “Technical issues are part of GPU programming!”
- Pivot to Colab - Have link ready to share
- Use shared screen - Show on your working setup
- Ask for help - “Anyone else seeing this? Let’s solve it together!”
If Discussion Stalls
- Ask specific people - “Sarah, what did you think about…?”
- Use analogies - “Think of it like a highway with many lanes…”
- Draw it out - Use whiteboard/shared drawing tool
- Share screen - Show relevant documentation
If Running Behind
- Cut exercise variations - Stick to basic hello_cuda.cu
- Shorten discussions - Save complex questions for “parking lot”
- Focus on key takeaways - GPU vs CPU, basic terminology
- Promise follow-up - “We’ll dive deeper into this next week”
✅ Success Checklist
You know you’re ready when:
- You can compile and run hello_cuda.cu without looking at notes
- You can explain GPU vs CPU differences in 2 minutes
- You have working Colab backup ready
- You’re genuinely excited to share GPU programming
Remember: Perfect is the enemy of good. Your participants want to learn, not judge your presentation skills. Focus on creating a supportive, collaborative environment where everyone feels comfortable asking questions and making mistakes.
You’ve got this! 🚀