When a job fails on an HPC cluster, your first instinct might be to rerun it and hope for a different outcome. That rarely works. The real answers are almost always sitting quietly in your job logs.
Understanding how to read those logs effectively can save hours of guesswork and help you fix issues faster and more confidently.
Start With the Basics: Exit Codes
Every job finishes with an exit code. This is the simplest signal of what happened.
- 0 means success
- Non-zero values indicate failure
In Slurm, you will often see something like:
ExitCode=1:0
The first number is the job’s exit status, and the second is the signal. If the signal is non-zero, it usually points to something more abrupt, like a kill or crash.
Check Standard Output and Error Files
Slurm writes logs to files like:
slurm-<jobid>.out
Or custom paths defined in your job script:
#SBATCH --output=job.out #SBATCH --error=job.err
These files are your primary source of truth.
- stdout shows normal program output
- stderr shows warnings, errors, and crashes
Always read stderr first when debugging.
Look for the First Error, Not the Last
A common mistake is focusing on the last line of the log. In reality, the root cause often appears much earlier.
For example:
File not found: input.dat Segmentation fault (core dumped)
The segmentation fault is just a consequence. The missing file is the real issue.
Memory Issues: Subtle but Common
Memory problems show up in different ways depending on how the system enforces limits.
Typical signs include:
- Out Of Memory
- Killed
- oom-kill event
In Slurm, you might also see:
slurmstepd: error: Detected 1 oom-kill event(s)
If this happens, your job likely exceeded its allocated memory. Increase --mem or optimize memory usage.
Node-Level Failures vs Application Errors
Not every failure is your fault.
Application Errors
- Segmentation faults
- Python tracebacks
- Missing libraries
These point to issues in your code or environment.
System or Node Issues
- Block device required
- I/O error
- Node unreachable messages
These suggest problems with the compute node, filesystem, or scheduler.
If multiple jobs fail on the same node, it’s a strong signal of a node issue.
Environment and Dependency Problems
A job might fail simply because something isn’t loaded.
Look for:
command not found module: not found libXYZ.so: cannot open shared object file
These errors usually mean:
- Missing modules
- Incorrect environment setup
- Wrong software versions
Double-check your module loads and environment variables.
MPI and Multi-Node Clues
For parallel jobs, logs can get noisy. Focus on patterns:
- Rank-specific failures
- Communication errors
- Timeouts
Examples include:
MPI_ABORT was invoked NCCL error connection timed out
These often point to network issues, misconfiguration, or mismatched libraries.
Timing and Resource Clues
Sometimes the issue isn’t a crash, but inefficiency or limits.
Look for:
- Jobs stopping exactly at walltime
- Slow startup or long idle times
- Uneven resource usage
Slurm accounting tools like sacct and seff can complement logs and give a clearer picture.
Build a Debugging Habit
Instead of reacting randomly to failures, follow a consistent approach:
- Check exit code
- Read stderr from top to bottom
- Identify the first real error
- Correlate with resource usage and job settings
- Verify environment and dependencies
Over time, patterns become familiar, and debugging gets faster.
Final Thoughts
Logs are not just noise. They are structured clues about what went wrong and why.
The more time you spend understanding them, the less time you waste guessing. In HPC environments, that difference matters.