How do you scale neural network training to multiple GPUs?

Scaling neural network training to multiple GPUs involves distributing computation and data across devices to accelerate training. The three main approaches are data parallelism, model parallelism, and pipeline parallelism. Data parallelism is the most common method, where each GPU holds a copy of the entire model and processes a different subset of the training data. Gradients are averaged across GPUs to synchronize model updates. Frameworks like PyTorch’s DistributedDataParallel and TensorFlow’s MirroredStrategy automate this process, allowing developers to scale training with minimal code changes.

Model parallelism splits the model itself across GPUs, which is useful when the model is too large to fit on a single GPU. For example, a transformer model with billions of parameters might split its layers across multiple devices. Each GPU computes a portion of the forward and backward passes, passing intermediate outputs between devices. This requires careful management of data movement and synchronization. Libraries like Megatron-LM and Mesh-TensorFlow provide tools to partition models efficiently. Pipeline parallelism, a subset of model parallelism, divides the model into stages (e.g., groups of layers) and processes micro-batches of data sequentially across stages to overlap computation and communication.

To implement multi-GPU training effectively, developers need to handle communication between GPUs, optimize batch sizes, and manage memory. Frameworks like PyTorch use the NVIDIA Collective Communication Library (NCCL) for fast GPU-to-GPU communication. Batch sizes should be scaled proportionally to the number of GPUs (e.g., 8 GPUs with a base batch size of 32 would use 256 total). Gradient accumulation can help maintain stability with large batches. Tools like NVIDIA’s DeepSpeed or Horovod simplify distributed training by automating gradient synchronization and mixed-precision optimization. For example, DeepSpeed’s Zero Redundancy Optimizer (ZeRO) reduces memory usage by partitioning optimizer states across GPUs, enabling training of models like GPT-3 at scale.