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For example, even Research Computing has no way to restore deleted data on the scratch3 filesystem. See File Transfer. See Filesystem Quotas. If you want to run a program called Analyze that you would run like this from the command line:. The job will be run in the priority partition for one hour, using 1 core, and 2GiB of memory. The output for the job will go to a filename called analyze. Note : A job gets 1 GiB memory if you don't explicitly ask for more or less , and 1 GiB is plenty for many applications. Your job will start faster the less memory you ask for. So only ask for extra memory if you need it — i. Please reference the Using Slurm Basic page for a longer introduction to what it means to submit jobs to O2.
If you want to debug or compile code, where you'll need to run a bunch of different programs one at a time, the fastest way to get started is to request an interactive job:. This will start an interactive job with a two hour time limit. From here, you can compile applications or run programs. Note that there are still limits on the amount of CPU or memory resources available to you. Your job will be limited to the actual number of core s you request; unless specified, a job will be allocated 1 core by default. Additionally, your job will be killed if you try to use more memory than what you have requested. See How to choose a partition in O2. Probably not, though the dispatch time of the job depends on the job priority, the resources you've requested, and the current availability of cluster resources.
For example, if the cluster is very busy and you need GiB of RAM for your analysis, your job will pend for a while, as you're essentially asking for a whole compute node to be empty. You can reference the Job Priority page, which details the six factors contributing to a job's priority. The most important factors for the average O2 user are: age increases the longer your job sits in the queue , partition jobs in interactive and priority partitions will be dispatched first , and fairshare tracks the resource you recently used and compares those with the fair share of computational resources available for each user. Your fairshare will deplete with more usage, but will fully rebound within two days of no cluster usage. When the cluster is busy, your jobs will pend longer the lower your fairshare is.
The best job submission strategy to choose depends on many factors. If each job only requires 1 CPU core and 1GiB or less of memory, then running the jobs separately will usually be faster. If you have any questions about optimizing your workflows, then please contact us! Please contact us and we'll be happy to work with your needs. You can use the O2sacct command to get detailed information on resource usage of your completed jobs, including how long the job took. See here for an introduction on using O2sacct. By running test versions of your workflow to make sure that the process is correct , you can get a sense of how long the full workflow will run. Remember that jobs can die for a variety of reasons so it's always best to design your workflow so you can quickly recover if it gets interrupted.
Please contact us if you would like help with this. Especially if you are just running something once, it's fine to overestimate the runtime limit. If you have a large batch of very short running jobs, the time to process the job submissions will be substantially longer than actually running the jobs, and this may slow the cluster down for everyone. You can write a script to batch sets of jobs together. Please contact us if you want assistance with this process.
Another option is to start a session in the interactive partition, and run the many short running jobs in that session. Use the --mem parameter in your job submission command with the amount of memory you want to request. See the Using Slurm Basic page for more information. This is related to a new change on O2. For more information, check the question - How do I use the --account argument when submitting jobs?
See this page for information on using O2squeue. For further job troubleshooting tips for pending jobs, please refer to the "Slurm Job Reasons" and the "Jobs that never start" sections of the Troubleshooting Slurm Jobs page. Probably because it ran too long or used too much memory. This can also occur if your job uses resources incorrectly e. See the "Exceeded run time" section of Troubleshooting Slurm jobs for more information on avoiding this error.
If you use more memory than you reserved or more than the default memory of 1GiB, if you didn't explicitly ask for a certain amount then your job will be killed. In output from the sacct command, this job will be seen as CANCELLED by 0 , which differentiates it as a job killed by the scheduler instead of a job killed by the user you will see another number representing your user id instead of 0 in this case. See the "Exceeded requested memory" section of Troubleshooting Slurm Jobs for more information.
If you're seeing that in a job output, that means your job was killed because it exceeded the memory allocation you requested. Simply request more memory with --mem or --mem-per-cpu , and eventually your job will complete successfully or you'll run out of available memory to request, in which case you should contact Research Computing for next steps. Either of these problems can be due to going over the GiB quota in your home directory, or over the set quota in a shared group directory. Please read here for more information.
If we moved jobs that exceeded a time limit, users could inappropriately take advantage of the scheduling system by always submitting to the shortest time-limited partition to get quick job execution, after which their long-running jobs would cascade through the partitions with longer limits. This is likely because you're using sbatch --wrap , or sbatch without writing a script. Enrique Rodriguez has written: 'Offentlig inkomstexpansion' -- subject s : History, Taxation. Hernandez-Rodriguez has written: 'Splendors of Latin cinema'.
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