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By default, your job will ask for 1 CPU core (one batch slot) and will allow maximum of 4GB of RAM. If you your job exceed the RAM limit, your job will be killed. If you need more CPU core or RAM, you can ask for more than one CPU cores when submitting your jobs. For example:
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SLAC ATLAS users have their own dedicate LSF queue and resource. They can also use the "general fairshare" queues. The latter are available to everyone at SLAC.
Dedicated LSF resource for ATLAS users
SLAC ATLAS users can run jobs in a dedicated LSF queue "atlas-t3". The following command show who can use the dedicate LSF resource, and who can add/remove users to the dedicated resource.
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With the two "&" at the end of the task lines (task1 and task2), the two tasks run simultaneously. If you want them to run sequentially, remove the two "&".
Batch resource for general users
If you remove the above "#BSUB -q atlas-t3" directive, your job will be submitted to a general "fair share" queue. This is one of a group of batch queues (short, medium, long and xlong). LSF will choose the queue for you based on the wall time specified. The general "fair share" queues are a much larger pool of resource. They are available to everyone at SLAC. They are heavily loaded most of the time.
Tips:
Kill all your jobs
If you made a mistake and want to kill all your batch jobs, use the following command
| Code Block |
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bjobs | grep -v JOBID | awk '{print "bkill", $1}' | sh |
Check the load of the cluster
To check how many jobs are currently running and pending in atlas-t3 queue:
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You can also check the Ganglia monitoring page to see the load on the cluster.
Use the batch slots efficiently
If you find that your batch job is not resource efficient (CPU/wall time ratio is low AND memory (RA(M) usage is lower (less than 1.5GB, use "bjobs -l job_id" to find out)), try putting two tasks in one batch job (in the above script) and let them run in parallel. But be careful. Don't run too many tasks in a batch job in parallel because it is possible to overwhelm some components in the tightly coupled computing environment.
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