Submitting a job to Slurm, the popular workload manager, only to find it cancelled can be frustrating. This guide delves into the common reasons behind Slurm job cancellations, offering troubleshooting steps and preventative measures. Understanding these causes empowers you to optimize your Slurm submissions and avoid future cancellations.
Common Reasons for Slurm Job Cancellation
Several factors can lead to a Slurm job being cancelled. Let's explore the most frequent culprits:
1. Resource Allocation Issues:
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Insufficient Resources: Your job may request more resources (CPU cores, memory, nodes) than are currently available in the Slurm cluster. This is a primary reason for cancellations, especially during peak usage periods. Carefully assess your job's resource requirements and adjust them based on cluster capacity. Check the Slurm
sinfocommand for available resources. -
Resource Conflicts: Slurm might cancel your job if it conflicts with other jobs requiring the same resources. This often happens when multiple users submit resource-intensive jobs simultaneously. Consider specifying node exclusivity using
--exclusiveor adjusting your job'sconstraintoptions to mitigate this. -
Node Failures: Hardware failures on allocated nodes can lead to job cancellation. Slurm typically attempts to reschedule the job on a healthy node, but if the problem persists, the job might remain cancelled. Monitor system logs for hardware-related error messages.
2. Time Limits and Walltime Exceeded:
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Walltime Exceeded: You might have specified a shorter walltime (maximum execution time) than your job actually requires. Slurm will automatically cancel the job once the walltime limit is reached. Accurately estimate your job's runtime and adjust the
--timeparameter accordingly. -
Step Time Limits: If your job involves multiple steps, each step might have individual time limits. Exceeding these limits for any step can trigger cancellation of the entire job. Review your script and set realistic time limits for each step.
3. Job Dependencies and Errors:
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Failed Dependencies: Your job might depend on other jobs completing successfully. If a dependency fails, Slurm will typically cancel your job to prevent downstream issues. Ensure your dependencies are correctly defined and working as expected.
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Job Script Errors: Errors within your job submission script (e.g., syntax errors, missing files) can lead to cancellation. Thoroughly test your script before submitting it to Slurm. Examine the Slurm job output for error messages.
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Program Errors: Errors within the program executed by your job can also cause cancellation. Debugging your code and handling potential errors within the program is crucial.
4. Account Issues and Quotas:
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Account Limits: Your Slurm account might have reached its usage limits (e.g., CPU time, memory usage). Slurm might prevent you from submitting further jobs until your usage falls below the defined limits. Check your account usage with
sacct. -
Quota Exceeded: Exceeding your allocated quota for resources (e.g., CPU time, memory, storage) can lead to job cancellations. Contact your system administrator to request an increase in your quota if necessary.
Troubleshooting and Prevention
To prevent future cancellations, follow these best practices:
- Carefully estimate resource requirements: Overestimate slightly to account for unexpected spikes in resource usage.
- Test your job script thoroughly: Identify and fix errors before submitting to Slurm.
- Monitor your job's progress: Use
squeueandsacctto check the status of your jobs. - Review Slurm logs: Examine job output files and Slurm logs for error messages.
- Contact your system administrator: If you suspect system-level issues, consult your administrator for assistance.
By understanding these common causes and implementing proactive measures, you can significantly improve the success rate of your Slurm jobs and avoid the frustration of unexpected cancellations. Remember to always consult your specific Slurm cluster's documentation for detailed information and specific configurations.
