On Optimizing Checkpoint Restoration for HPC Applications: Leveraging Merkle Trees and Asynchronous I/O

Efficient checkpoint restoration is critical in high-performance computing (HPC) and AI applications, where slow recovery times disrupt workflows, waste resources, and hinder reproducibility. This work introduces a Merkle tree checkpoint restoration method to accelerate failure recovery and improve explainability. Our method integrates asynchronous I/O via the Liburing library to optimize scattered reads in HPC applications. Tested on the Polaris system at Argonne National Laboratory, it exhibits lower restoration time and memory consumption than state-of-the-art checkpoint restoration methods, reaching near-full efficiency with duplicated data. Our work advances scalable and efficient checkpointing solutions for HPC, ensuring reliable and fast failure recovery for large-scale simulations.