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60,000 Times Slower Python

60,000 Times Slower Python

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 The End of Moore's Law and the Future of Computing PerformanceThe Automobile Industry Parallel
  • 1960s: Focus on power over efficiency (muscle cars, gas guzzlers)
  • Evolution through Japanese efficiency, turbocharging, to electric vehicles
  • Similar pattern now happening in computing
The Python Performance Crisis
  • Matrix multiplication example: 7 hours vs 0.5 seconds
  • 60,000x performance difference through optimization
  • Demonstrates massive inefficiencies in modern languages
  • Industry was misled by Moore's Law into deprioritizing performance
Performance Improvement Hierarchy

  1. Language Choice Improvements:

    • Java: 11x faster than Python
    • C: 50x faster than Python
    • Why stop at C-level performance?

  2. Additional Optimization Layers:

    • Parallel loops: 366x speedup
    • Parallel divide and conquer
    • Vectorization
    • Chip-specific features
The New Reality in 2025
  • Moore's Law's automatic performance gains are gone
  • LLMs make code generation easier but not necessarily better
  • Need experts who understand performance optimization
  • Pushing for "faster than C" as the new standard
Future Directions
  • Modern compiled languages gaining attention (Rust, Go, Zig)
  • Example: 16KB Zig web server in Docker
  • Rethinking architectures:
    • Microservices with tiny containers
    • WebAssembly over JavaScript
    • Performance-first design
Key Paradigm Shifts
  • Developer time no longer prioritized over runtime
  • Production code should never be slower than C
  • Single-stack ownership enables optimization
  • Need for coordinated improvement across:
    • Language design
    • Algorithms
    • Hardware architecture
Looking Forward
  • Shift from interpreted to modern compiled languages
  • Performance engineering becoming critical skill
  • Domain-specific hardware acceleration
  • Integrated approach to performance optimization

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