Outcome#
You can interpret sacct and seff, calculate requested resources, distinguish
successful from efficient, and adjust only the next request using representative
evidence.
Concept#
Large “just in case” requests reduce availability and may wait longer without improving a serial or I/O-bound program. A completed state alone does not prove appropriate resource use.
Worked Example#
The proposed request follows measured utilization and does not enlarge resources merely to hide an error.
A correct example uses these decisions:
- Which value helps estimate memory actually used? MaxRSS, interpreted with the job steps and units.
- A job exits immediately with a Python import error. What should you optimize first? Fix and test the software environment before changing resources.
- A representative serial job requested 4 CPUs, 16 GiB per CPU, and 2 hours. It completed in 20 minutes with 22% CPU efficiency and 9 GiB MaxRSS. Which next test is justified? Test 1 CPU, 16 GiB total memory, and 45 minutes on a representative input.
Common Trap#
Treating requested memory as measured memory, or increasing every resource after a software failure.
If Blocked#
Do not increase resources when fields are unclear. Use Euler resource optimization and ask for help for MPI/multiprocess workloads, highly variable inputs, or disagreeing metrics.
Useful references:
Understand Before Accepting AI Output#
Verify arithmetic, per-CPU versus total memory, representativeness, and the parallelism claim. An agent cannot infer scaling merely from CPU availability.