Available Partitions

Slurm divides resources into partitions, sometimes called queues. Each partition targets specific hardware or workloads.

Partition

Max Time (hrs)

Cores per Node

Max Memory

GPUs

Use Case

express

8

4

8 GB

—

Short tests, debugging, interactive work (Jupyter, RStudio)

shared

36

32

4 GB/core

—

Small-scale workflows with shared node usage

parallel

72

48

192 GB

—

Dedicated nodes for large parallel jobs

bigmem

48

48

1.5 TB

—

Memory-intensive jobs with special allocation

a100

72

48

192 GB

4× A100 (40 GB)

GPU workflows

ica100

72

64

256 GB

4× A100 (80 GB)

GPU workflows

mig_class

24

64

256 GB

12× MIGs (20 GB)

Classroom GPU usage

l40s

24

64

256 GB

8× L40s (48 GB)

High-performance GPU workloads

Partition Descriptions

express

Express is designed for short-running jobs, including tests, debugging, or interactive sessions (e.g., Jupyter notebooks, RStudio).

  • CPU Limit: Up to 4 cores

  • Memory Limit: Up to 8 GB per job

  • Node Sharing: Yes (shared with other jobs)

  • Max Runtime: 8 hours

shared

Shared is designed for a mixture of jobs, from single-core sequential jobs to small parallel jobs (less than 32 cores).

  • Memory: 4 GB per core

  • Node Sharing: Yes (shared with other jobs)

  • Best For: Smaller scale workflows

  • Max Runtime: 36 hours

parallel

Parallel is designed only for jobs requiring 48 cores or more.

  • Nodes: Single or multiple (up to 75)

  • Node Sharing: No (dedicated)

  • User Responsibility: All cores must be utilized

  • Max Runtime: 3 days

bigmem

Bigmem is designed for memory-intensive workflows.

  • Memory: 1.5 TB per node

  • Requirements:

    • Slurm allocation: <PI_NAME>_bigmem (e.g., jsmith123_bigmem)

    • QoS: qos_bigmem

  • Max Runtime: 2 days

a100

A100 is for GPU-enabled workflows.

  • GPUs: 4× NVIDIA A100 (40 GB each)

  • Requirements:

    • Slurm allocation: <PI_NAME>_gpu (e.g., jsmith123_gpu)

    • QoS: qos_gpu

  • Max Runtime: 3 days

ica100

ICA100 is for GPU-enabled workflows using upgraded A100 cards.

  • GPUs: 4× NVIDIA A100 (80 GB each)

  • Requirements:

    • Slurm allocation: <PI_NAME>_gpu (e.g., jsmith123_gpu)

    • QoS: qos_gpu

  • Max Runtime: 3 days

mig_class

Mig_class is intended for classroom GPU workflows.

  • GPUs: 4× NVIDIA A100 (80 GB each), segmented into 12× 20 GB MIGs

  • Requirements:

    • Slurm allocation: <class_name>-<PI_NAME> (e.g., cs601-jsmith123)

    • QoS: mig_class

  • Max Runtime: 1 day

l40s

L40s is designed for GPU workflows using L40s GPUs.

  • GPUs: 8× NVIDIA L40s (48 GB each)

  • Requirements:

    • Slurm allocation: <PI_NAME>_gpu (e.g., jsmith123_gpu)

    • QoS: qos_gpu

  • Max Runtime: 1 day

Viewing Partition Configuration

You can view details about any partition with the scontrol command. This is helpful to check limits, available nodes, default memory settings, and which QoS values are allowed or denied.

  • Use scontrol show partition without any arguments to see all partitions.

  • To find which QoS values are allowed or blocked in a partition, look at QoS= and DenyQos=.

Example:

scontrol show partition=shared

Sample Output:

PartitionName=shared
   AllowGroups=ALL AllowAccounts=ALL DenyQos=qos_gpu,qos_bigmem
   AllocNodes=ALL Default=NO QoS=shared
   DefaultTime=01:00:00 DisableRootJobs=NO ExclusiveUser=NO GraceTime=0 Hidden=NO
   MaxNodes=1 MaxTime=1-12:00:00 MinNodes=1 LLN=NO MaxCPUsPerNode=128 MaxCPUsPerSocket=UNLIMITED
   Nodes=sr[07-47]
   PriorityJobFactor=1 PriorityTier=2 RootOnly=NO ReqResv=NO OverSubscribe=YES:4
   OverTimeLimit=NONE PreemptMode=OFF
   State=UP TotalCPUs=2624 TotalNodes=41 SelectTypeParameters=NONE
   JobDefaults=(null)
   DefMemPerCPU=4000 MaxMemPerCPU=4000
   TRES=cpu=2624,mem=10250G,node=41,billing=2624
   TRESBillingWeights=CPU=1.0,Mem=0.00025M

Key Fields to Note

  • MaxTime: The maximum wall-clock time allowed for jobs in this partition.

  • DefMemPerCPU: The default memory available per core (can be overridden with –mem or –mem-per-cpu).

  • Nodes: The physical nodes available for this partition.

  • OverSubscribe: Indicates if jobs can share nodes.

  • DenyQos: QOS values that are explicitly blocked from this partition.

  • TRES: Total Resources (CPUs, memory, nodes) assigned to this partition.

Helpful Tips

  • You can view the current load on each partition with:

    [root@login03 ~]# sinfo -s
PARTITION AVAIL  TIMELIMIT   NODES(A/I/O/T) NODELIST
parallel*    up 3-00:00:00     687/24/9/720 c[001-720]
v100         up 3-00:00:00          0/1/0/1 gpu01
a100         up 3-00:00:00        15/2/0/17 gpu[02-18]
ica100       up 3-00:00:00         8/2/0/10 icgpu[01-10]
bigmem       up 2-00:00:00        16/9/0/25 bigmem[01-25]
mig_class    up 1-00:00:00          3/0/0/3 gpuz[01-03]
express      up    8:00:00          0/5/0/5 sr[01-05]
shared       up 1-12:00:00       12/29/0/41 sr[07-47]
l40s         up 1-00:00:00          1/2/1/4 l[01-04]
emr          up 7-00:00:00        11/6/1/18 er[01-18]

    This provides a summary view of each partition’s usage and availability.

  • To see the list of available partitions and their state:

    sinfo -o "%P %.5D %.10t %.10l %.6c %.10m"

    This will output:

    • Partition name

    • Node count

    • State (idle/alloc/mix)

    • Max time

    • CPUs per node

    • Memory

Partition Best Practices

  • Use –partition= to explicitly request a partition in your batch script.

  • Avoid defaulting to GPU partitions unless required — this helps ensure fair usage.

  • Read memory policies carefully (e.g., shared nodes have 4 GB/core).

  • Always pair GPU partitions with the appropriate QOS and allocation account.