Building High-Performance ML Systems

Introduction

These are my collected insights and lessons learned from building and researching ML systems, particularly focusing on performance optimization and scalability.

Key Principles

1. System Design

• Separation of concerns
• Resource management
• Failure handling
• Monitoring and observability

2. Performance Optimization

• Memory hierarchy awareness
• Computation-communication overlap
• Workload characterization
• Resource utilization

Common Patterns

1. Data Movement
2. Caching strategies
3. Prefetching

4. Data layout optimization

5. Computation

6. Operator fusion
7. Kernel optimization
8. Hardware acceleration

Lessons Learned

1. Profile before optimizing
2. Consider end-to-end performance
3. Design for debuggability

Future Directions

1. AutoML for systems
2. Hardware-software co-design
3. Adaptive optimization

References

1. System Design Papers
2. Performance Analysis Tools
3. Case Studies