AI in Multiple GPUs: Gradient Accumulation & Data Parallelism

Gradient Accumulation enables training with larger effective batch sizes by sequentially processing micro-batches and accumulating their gradients before one optimization step. Distributed Data Parallelism (DDP) enhances this by processing these micro-batches in parallel across multiple GPUs. In a DDP setup, each GPU calculates gradients on its own data portion, and these gradients are then averaged across all devices using an All-Reduce operation to ensure model parameters remain synchronized. Combining DDP with Gradient Accumulation further improves efficiency by reducing the frequency of inter-GPU communication, which is crucial for training large-scale models.