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60,000 Times Slower Python
Manage episode 468099614 series 3610932
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The End of Moore's Law and the Future of Computing Performance
The 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
Language Choice Improvements:
- Java: 11x faster than Python
- C: 50x faster than Python
- Why stop at C-level performance?
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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