Partitions

Partitions

A partition is a logical collections of nodes that comprise different hardware resources and limits to help meet the wide variety of jobs that get scheduled on the cluster. Occasionally, the Research Computing team might need to make updates to the partitions based on monitoring job submissions to help reduce job wait times. As our cluster grows, changes to the partitions also help to ensure the fair, efficient distribution of resources for all jobs being submitted to the cluster.

On Discovery, there are several partitions.

• General Access: i.e., debug, express, short, gpu
• Application Only: i.e., long, large, multigpu
• Courses: Dedicated to the use of students and instructors for the duration of a course.
• PI Owned: Private partitions that include hardware that a PI owns; can only be accessed by members of the PIs’ group.

The general access and application only partitions span the hardware on the cluster, with gpu and multigpu spanning the GPUs on the cluster and the other partitions spanning the CPUs. For example, if you use the debug partition you are using the same hardware as short, with different time, job, and core limits. Refer to the tables below for detailed information on the current partitions.

In the following table, the Running Jobs Per User/Per Research Group, Core and RAM limits are set per user, across all running jobs (not pending). Please keep in mind that the number of running jobs is limited by the available resources on the cluster at the time of the job submission and may not adhere to the number stated below.

NOTE: It is possible for you to see the message, job violates accounting/QOS policy in the output of squeue -u $USER, even if you submitted fewer jobs than the number of submitted jobs indicated in the table below. This implies that Slurm has reached the hard-coded limit of 10,000 total jobs/per account at that time. Here the account refers to the Slurm account that you, as a cluster user, are associated with. So you will continue to receive this error message until some of your jobs start running and/or get completed before you can submit more jobs.

Slurm commands allow you to view information about the partitions. Three commands that can show you partition information are sinfo, sacct, and scontrol. The following are common options to use with these commands:

To specify a partition when running jobs, use the option --partition=<partition name> with either srun or sbatch. When using a partition with your job and specifying the options of --nodes= and --ntasks= and/or --cpus-per-task=, make sure that you are requesting options that best fit your job.

Please Note: Requesting the maximum number of nodes or tasks will not make your job run faster or give you higher priority in the job queue. This can actually have the opposite effect on jobs that are better suited to running with smaller requirements, as you have to wait for the extra resources that your job will not use. 

You should always try to have job requests that will attempt to allocate the best resources for the job you want to run. For example, if you are running a job that is not parallelized, you only need to request one node (--nodes=1). For some parallel jobs, such as a small MPI job, you can also use one node (--nodes=1) with the –-ntasks= option set to correspond to the number of MPI ranks (tasks) in your code. For example, for a job that has 12 MPI ranks, request 1 node and 12 tasks within that node (--nodes=1 –-ntasks=12). If you request 12 nodes, Slurm is going to run code between those nodes, which could slow your job down significantly if it is not optimized to run between nodes.

If your code is optimized to run on more than two nodes and needs less than one hour to run, you can use the express partition. If your code needs to run on more than 2 nodes for more than one hour, you should apply to use the large partition. See the section Partition Access Request below for more information.

If you need access to the large, long, or multigpu partition, you need to submit a Partition Access Request; please note that access is not automatically granted. You will need to provide details and test results that demonstrate your need for access for these partitions.

If you need temporary access to multigpu to perform testing before applying for permanent access, you should also submit the Partition Access Request. All requests are evaluated by members of the RC team. Once reviewed and approved, you will be added to have access to that partition.