AI in Multiple GPUs: Point-to-Point and Collective Operations

PyTorch's `torch.distributed` module provides communication patterns for multi-GPU AI workloads, distinguishing between synchronous (blocking) and asynchronous (non-blocking) operations. Asynchronous calls can improve performance by overlapping computation with communication, but require careful handling. The fundamental communication types include point-to-point operations like `send` and `recv` for direct data transfer between two specific GPUs. Additionally, one-to-all collective operations like `broadcast` (copying data to all GPUs) and `scatter` (distributing data chunks across all GPUs) are explained as building blocks for distributed training.