ML Infrastructure Programming

Compilation Pipeline#

1. Frontend Processing

   • Python AST parsing and analysis
   • Type inference and validation
   • High-level optimization passes
   • Intermediate representation generation

2. Backend Code Generation

   • Target-specific code generation
   • Memory layout optimization
   • Instruction scheduling
   • Register allocation

3. Runtime Optimization

   • Just-in-time compilation
   • Dynamic kernel selection
   • Performance monitoring
   • Adaptive tuning

Memory Management#

1. Memory Hierarchy Awareness

   • Automatic memory placement decisions
   • Cache-friendly access patterns
   • Shared memory utilization
   • Memory coalescing optimization

2. Memory Safety

   • Bounds checking and validation
   • Automatic memory management
   • Leak detection and prevention
   • Memory usage profiling

Performance Optimization Techniques#

1. Kernel Fusion

   • Automatic detection of fusion opportunities
   • Memory bandwidth reduction
   • Kernel launch overhead elimination
   • Improved cache utilization

2. Parallelization Strategies

   • Thread-level parallelism
   • Data parallelism
   • Pipeline parallelism
   • Hybrid approaches