CheckpointRestart

Checkpoint restart is a fault tolerance technique used to save and restore the state of a running computation so it can resume after a failure, crash, or migration. A checkpoint captures enough of a program’s state to restart execution from a known point, typically including memory contents, CPU registers, and open resources such as files or network connections. In distributed applications it may also capture the state of communication channels and synchronization data to restore a consistent global snapshot.

Checkpointing approaches are broadly categorized as application-level and system-level. In application-level checkpointing, the program or runtime library knows its own state and can selectively save essential data. System-level approaches rely on the operating system or virtualization layer to persist process state. Checkpointing can be coordinated, where all processes take checkpoints at a synchronized point, or uncoordinated, where processes checkpoint independently. Coordinated schemes simplify restart but can incur higher overhead, while uncoordinated schemes require more complex recovery to avoid inconsistencies.

Checkpoints may be full or incremental, and can be stored on local disks, parallel file systems, or cloud storage. Support for asynchronous I/O reduces disruption during computation. Tools and frameworks include DMTCP, BLCR, CRIU, and MPI-centric implementations; many HPC and cloud platforms provide their own checkpoint facilities.

On restart, the system reloads the saved state and resumes execution. In distributed environments, restart may reestablish communications and reinitialize external resources. Checkpoint restart enables long-running simulations, resilience to hardware failures, and live migration or workload balancing in some environments.

Challenges include performance overhead during normal operation, ensuring consistency for in-flight messages, large state sizes, and storage costs. Ongoing research seeks to minimize pause times, enable transparent checkpointing, and improve scalability.