GPU Snapshot: Checkpoint Offloading for GPU-Dense Systems

Future High-Performance Computing (HPC) systems will likely be composed of accelerator-dense heterogeneous computers because accelerators are able to deliver higher performance at lower costs, socket counts and energy consumption. Such acceleratordense nodes pose a reliability challenge because preserving a large amount of state within accelerators for checkpointing incurs significant overhead. Checkpointing multiple accelerators at the same time, which is necessary to obtain a consistent coordinated checkpoint, overwhelms the host interconnect, memory and IO bandwidths. We propose GPU Snapshot to mitigate this issue by: (1) enabling a fast logical snapshot to be taken, while actual checkpointed state is transferred asynchronously to alleviate bandwidth hot spots; (2) using incremental checkpoints that reduce the volume of data transferred; and (3) checkpoint offloading to limit accelerator complexity and effectively utilize the host. As a concrete example, we describe and evaluate the design tradeoffs of GPU Snapshot in the context of a GPU-dense multi-exascale HPC system. We demonstrate 4–40X checkpoint overhead reductions at the node level, which enables a system with GPU Snapshot to approach the performance of a system with idealized GPU checkpointing.


Kyushick Lee (University of Texas at Austin)
Timothy Tsai (NVIDIA)
Mattan Erez (University of Texas at Austin)

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