self-diagnosing-resource-use

To check Slurm job resource usage after completion, analyze accounting data from sacct and seff to quantify CPU efficiency, memory consumption, GPU utilization, and elapsed wall time for your finished jobs.

Diagnose low GPU utilization in HPC jobs by reviewing nvidia-smi data alongside sacct records to identify under-utilized GPU allocations and determine whether your job script requests more GPUs than it actively uses.

Right-size CPU and memory requests for Slurm job scripts by comparing allocated resources against actual usage metrics from seff, then adjusting future submissions using practical thresholds to prevent wasteful over-requesting.

Yes, you can use sacct and seff to evaluate wall-time usage on an HPC cluster by reviewing elapsed time metrics to determine if your job submission overestimated the required runtime.

Use practical efficiency thresholds derived from CPU, memory, GPU, and wall-time usage data to guide adjustments, ensuring future HPC resource allocations match actual workload demands and promote cluster fairness.

A Slurm job shows high memory allocation but low usage when the requested memory limit exceeds actual consumption, a pattern identified through seff diagnostics that indicates you should reduce memory requests in future job scripts.