Uploaded June 2026 | Updated September 2026, 3 weeks ago
Checkmate: Zero Performance Overhead Model Checkpointing via Network Gradient Replication
Ankit Bhardwaj, Tufts University; Weiyang Wang, Jeremy Carin, Adam Belay, and Manya Ghobadi, Massachusetts Institute of Technology
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 34.5× 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 1.3× to 6.5× throughput compared to other systems.
View the full NSDI '26 program at usenix.org/conference/nsdi26/technical-sessions
Checkmate: Zero Performance Overhead Model Checkpointing via Network Gradient Replication
Ankit Bhardwaj, Tufts University; Weiyang Wang, Jeremy Carin, Adam Belay, and Manya Ghobadi, Massachusetts Institute of Technology
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 34.5× 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 1.3× to 6.5× throughput compared to other systems.
View the full NSDI '26 program at usenix.org/conference/nsdi26/technical-sessions
