ML Infrastructure Programming

Historical Context#

1. Early ML Programming (Pre-2015)

   • Custom CUDA kernels for specific operations
   • Low-level C++/CUDA development
   • Manual memory management and optimization
   • High barrier to entry for ML researchers

2. Framework Era (2015-2020)

   • High-level frameworks (TensorFlow, PyTorch)
   • Automatic differentiation and optimization
   • Focus on model development over infrastructure
   • Limited hardware-specific optimization

3. Modern Infrastructure Programming (2020+)

   • Domain-specific languages for ML
   • Hardware-aware compilation
   • Automated kernel optimization
   • Balance of productivity and performance

Current Challenges#

Performance vs. Productivity Trade-off:#

• Manual CUDA optimization offers maximum performance but requires deep expertise
• High-level frameworks are productive but may not utilize hardware optimally
• Need for solutions that bridge this gap

Hardware Diversity:#

• Multiple GPU architectures (NVIDIA, AMD, Intel)
• Specialized AI accelerators (TPU, IPU, neuromorphic)
• Memory hierarchy and bandwidth variations
• Different programming models and instruction sets