Advanced Data Parallelism Techniques for Large-Scale ML
Beyond Basic Data Parallelism
While standard data parallelism splits datasets across workers, advanced implementations focus on gradient optimization and communication efficiency.
Optimization Techniques
- Gradient compression
- Asynchronous updates
- Efficient all-reduce communication
These strategies reduce network overhead and improve training speed.
