LLM Optimization: Integration of Traditional Methods and New Paradigms
Core Message
LLM (Transformer) optimization requires more than just traditional optimization methodologies – new perspectives must be added.
1. Traditional Optimization Methodology (Left Side)
SW (Software) Optimization
- Data Optimization
- Structure: Data structure design
- Copy: Data movement optimization
- Logics Optimization
- Algorithm: Efficient algorithm selection
- Profiling: Performance analysis and bottleneck identification
Characteristics: Deterministic, logical approach
HW (Hardware) Optimization
- Functions & Speed (B/W): Function and speed/bandwidth optimization
- Fit For HW: Optimization for existing hardware
- New HW implementation: New hardware design and implementation
Characteristics: Physical performance improvement focus
2. New Perspectives Required for LLM (Right Side)
SW Aspect: Human-Centric Probabilistic Approach
- Human Language View / Human’s View
- Human language understanding methods
- Human thinking perspective
- Human Learning
- Mimicking human learning processes
Key Point: Statistical and Probabilistic Methodology
- Different from traditional deterministic optimization
- Language patterns, probability distributions, and context understanding are crucial
HW Aspect: Massive Parallel Processing
- Massive Simple Parallel
- Parallel processing of large-scale simple computations
- Hardware architecture capable of parallel processing (GPU/TPU) is essential
Key Point: Efficient parallel processing of large-scale matrix operations
3. Integrated Perspective
LLM Optimization = Traditional Optimization + New Paradigm
| Domain | Traditional Method | LLM Additional Elements |
|---|---|---|
| SW | Algorithm, data structure optimization | + Probabilistic/statistical approach (human language/learning perspective) |
| HW | Function/speed optimization | + Massive parallel processing architecture |
Conclusion
For effective LLM optimization:
- Traditional optimization techniques (data, algorithms, hardware) as foundation
- Probabilistic approach reflecting human language and learning methods
- Hardware perspective supporting massive parallel processing
These three elements must be organically combined – this is the core message of the diagram.
Summary
LLM optimization requires integrating traditional deterministic SW/HW optimization with new paradigms: probabilistic/statistical approaches that mirror human language understanding and learning, plus hardware architectures designed for massive parallel processing. This represents a fundamental shift from conventional optimization, where human-centric probabilistic thinking and large-scale parallelism are not optional but essential dimensions.
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Lechuck Park 