  To efficiently scale large model (LM) training, researchers transition from
data parallelism (DP) to hybrid parallelism (HP) on GPU clusters, which
frequently experience hardware and software failures. Existing works introduce
in-memory checkpointing optimizations that snapshot parameters to device memory
for rapid failure recovery. However, these methods introduce severe resource
competition between checkpointing and training, which can work under DP but can
hardly scale under resource-intensive HP. To ensure low checkpointing overhead
for hybrid-parallel training, this paper introduces a distributed in-memory
checkpointing system with near-zero in-memory saving overhead. It strives from
two aspects to mitigate the on-host resource competition caused by in-memory
checkpointing: (1) It introduces Hierarchical Asynchronous Snapshotting
Coordination in the checkpoint saving stage. This approach uses three-level
asynchronous on-device scheduling to enhance parallelism between snapshotting
and training, thereby minimizing snapshotting overhead. (2) It proposes Hybrid
In-memory Checkpoint Protection to enhance checkpoint completeness during
hardware failures. Unlike methods that require inter-node communications, which
may block training under HP, it creates intra-node redundancy with efficient
resource utilization, protecting training against hardware failures with
minimal overhead. With these methods, this work enables fast restart for failed
HP training with Distributed In-memory Checkpoint Loading, bypassing
inefficiencies in NFS reads. In our evaluation, we achieve zero in-memory
checkpoint saving overhead on Frontier while training Llama-2-34B on 256 MI250X
devices (512 GPUs).
