Queue Policies

5 minute read

Start Times

Start times are a great way to track your jobs:

squeue -u $USER --start

Start times are rough estimates based on the current state of the queue.

Partition Quotas

Each partition has a specific usecase. Below outlines each partition, it’s usecase, as well as any job/user/group limits that are in place. Empty boxes imply no limit, but is still limited by the next higher limit. Job limits are capped by user limits, and user limits are capped by group limits.

Partition NameUsecasePer-Group LimitPer-User LimitPer-Job LimitMax Job Time
batchCPU Intensive Workloads, Multithreaded, MPI, OpenMP256 Cores, 1TB memory64GB memory per Core 1,230 Days
intelCPU Intensive Workloads, Multithreaded, MPI, OpenMP256 Cores, 1TB memory64GB memory per Core 1,330 Days
epycCPU Intensive Workloads, Multithreaded, MPI, OpenMP256 Cores, 1TB memory64GB memory per Core 1,430 Days
shortShort CPU Intensive Workloads, Multithreaded, MPI, OpenMP256 Cores, 1TB memory64GB memory per Core, 2-hour time limit2 Hours
highmemMemory Intensive Workloads32 Cores, 1TB memory30 Days
gpuGPU-Enabled Workloads8 GPUs48 Cores, 512GB memory16 Cores, 256GB memory 1,530 Days

Attempting to allocate more member than a node can support, eg 500GB on an Intel node, will cause the job to immediately fail.

Limits are for actively running jobs, and any newly queued job that exceeds a limit will be queued until resources become available. In addition to the above limits, there is also a 512 core group limit that spans across all users in a group across all partitions. Upon request, a user or group’s quota can be extended temporarily, but only if sufficient CPU resources are available and with adequate justification. If you wish to request additional resources, please contact us at support@hpcc.ucr.edu with your justification.

Partition quotas can also be viewed on the cluster using the slurm_limits command.

Example Scenarios

Per-Job Limit

A job is submitted on the gpu partition. The job requests 32 cores.

This job will not be able to be submitted, as 32 cores is above the partition’s 16 core per-job limit.

Per-User Limit

A job is started on the highmem partition, requesting 32 cores.

This job will start successfully, as it is within the partition’s core limit.

A second job is submitted while the first job is still running. The new job is requesting 32 cores.

Because the user is already at their per-user core limit on the highmem partition, the second job will be queued until the first job finishes.

Per-Lab Limit

User A submits 2 jobs, one on the intel partition and one on the epyc partition, each requesting 256 cores.

Because per-user limits are per-partition, both jobs will be submitted and run in parallel.

User B submits a job on the epyc partition, requesting 16 cores.

Because user A is using all 512 cores within the lab, user B’s job will be queued until one of user A’s jobs finishes.


Users that have not submitted any jobs in a long time usually have a higher priority over others that have ran jobs recently. Thus the estimated start times can be extended to allow everyone their fair share of the system. This prevents a few large groups from dominating the queuing system for long periods of time.

You can see with the sqmore command what priority your job has (list is sorted from lowest to highest priority). You can also check to see how your group’s priority is compared to other groups on the cluster with the “sshare” command.

For example:


It may also be useful to see your entire group’s fairshare score and who has used the most shares:

sshare -A $GROUP --all

Lastley, if you only want to see your own fairshare score:

sshare -A $GROUP -u $USER

The fairshare score is a number between 0 and 1. The best score being 1, and the worst being 0. The fairshare score approches zero the more resource you (or your group) consume. Your individual consumption of resources (usage) does affect your entire group’s fiarshare score. The affects of your running/completed jobs on your fairshare score are halved each day (half-life). Thus, after waiting several days without running any jobs, you should see an improvment in your fairshare score.

Here is a very good explaination of fairshare.


The fairshare score and jobs queue wait time is used to calculate your job’s priority. You can use the sprio command to check the priority of your jobs:

sprio -u $USER

Even if your group has a lower fairshare score, your job may still have a very high priority. This would be likely due to the job’s queue wait time, and it should start as soon as possible regardless of fairshare score. You can use the sqmore command to see a list of all jobs sorted by priority.


Some small jobs may start before yours, only if they can complete before yours starts and thus not negatively affecting your start time.

Priority Partition

Some groups on our system have purchased additional hardware. These nodes will not be affected by the fairshare score. This is because jobs submitted to the group’s partition will be evaluated first before any other jobs that have been submitted to those nodes from a different partition.

  1. A 64GB-per-core limit is placed to prevent over allocating memory compared to CPUs. If more than a 64GB-per-core ratio is requested, the core count will be increased to match. ↩︎

  2. Allocatable memory per-node in the batch partition is limited to ~500GB to allow for system overhead. ↩︎

  3. Allocatable memory per-node in the intel partition is limited to ~450GB to allow for system overhead. ↩︎

  4. Allocatable memory per-node in the epyc partition is limited to ~950GB to allow for system overhead. ↩︎

  5. Allocatable memory per-node in the gpu partition is dependent on the node. 115GB for gpu[01-02], 500GB for gpu[03-04], 200GB for gpu05, 922GB for gpu06, 950GB for gpu[07-08] ↩︎

Last modified April 18, 2024: Update queue.md (9967c825f)