Gremlin and GCP HPC VM images/clusters

HPC workloads are frequently tightly coupled, which changes the arithmetic of node failure completely. In a loosely coupled job, losing one worker costs you that worker's share. In an MPI job on Google Cloud HPC where ranks communicate continuously, losing one node can fail the entire computation regardless of how many others stayed healthy.

That makes checkpointing the deciding factor, and checkpointing is usually tuned for throughput rather than resilience. Infrequent checkpoints mean less overhead and more work lost per failure. Teams pick an interval early, without measuring what a failure costs, then run it for years.

Install the Gremlin Agent on your compute nodes and Shutdown experiments produce the number that makes the tradeoff real: how much computation disappears when a node drops mid-run, and how long recovery takes. CPU and memory experiments reveal whether a single degraded node slows the entire job, which is common in tightly coupled work and invisible in aggregate metrics.

With that measurement, your checkpoint interval becomes an informed decision rather than a guess nobody has revisited since the cluster was built.