Checkmate: Zero-Overhead Model Checkpointing via Network Gradient Replication

TL;DR

Checkmate introduces a zero-overhead checkpointing system for deep neural network training that leverages network gradient replication to enable frequent, per-iteration checkpoints without slowing down training.

Contribution

It proposes a novel multicast abstraction and gradient replication method that allows continuous checkpointing during training without performance loss.

Findings

Achieves 5 to 34.5x more frequent checkpointing than existing systems.

Reduces repeated work per failure by 80% to 97.1%.

Maintains training throughput comparable to no-checkpoint baseline.

Abstract

This paper presents Checkmate, a system that enables per-iteration checkpointing in DNN training without any training slowdown. The traditional approach to checkpointing requires a pause in training to copy model states to a separate location, allowing the state to be restored in the event of failure. This approach fundamentally has a tradeoff between the frequency of checkpoints and the cost of a failure. We avoid this tradeoff; our key insight is that in data-parallel training, all information necessary to create a checkpoint already exists in the network as gradients. Our core contribution is a new multicast abstraction that simultaneously delivers gradients to a separate CPU-based shadow cluster. The shadow maintains a checkpoint by applying those gradients to a copy of the model. Our evaluation shows that Checkmate performs per-iteration checkpointing with training throughput comparable to an ideal no-checkpoint baseline. Checkmate achieves 5 to 34.5x more frequent checkpointing compared to state-of-the-art checkpointing systems, resulting in 80% to 97.1% reduction in repeated work per failure. At the same checkpointing frequency, Checkmate delivers 1.3x to 6.5x throughput compared to other systems.