Final answer:
ZeRO is a memory optimization technique proposed by NVIDIA to address memory fragmentation and improve memory efficiency in distributed deep learning training. It also mitigates compute-bound problems by leveraging techniques like gradient accumulation and offloading computations.
Step-by-step explanation:
ZeRO is a memory optimization technique proposed by NVIDIA that addresses memory fragmentation in distributed deep learning training. It is designed to mitigate the limitations posed by memory-bound problems in training large deep learning models.
ZeRO achieves this by partitioning the model and optimizer states across multiple devices or nodes, allowing for more efficient memory utilization. The key idea is to separate the optimizer states from the model weights and store them in a central location called the optimizer state repository. This reduces memory fragmentation by keeping the memory allocation pattern more contiguous, thereby improving memory efficiency and enabling larger model sizes.
Moreover, ZeRO exploits techniques like gradient accumulation and offloading certain computations to reduce the compute requirements during the backward pass. This helps alleviate compute-bound problems that can arise when training large models with limited computational resources.