Hybrid Parallelism Strategies for Large-Scale Deep Learning Training
Hybrid parallelism is a foundational technique in large-scale distributed deep learning, designed to orchestrate multiple parallelization dimensions—data, tensor, and pipeline—to maximize hardware utilization while scaling training across thousands of accelerators. Unlike monolithic parallel approac...