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What is Slurm?

Slurm (previously Simple Linux Utility for Resource Management), is a modern, open source job scheduler that is highly scaleable and customizable; currently, Slurm is implemented on the majority of the TOP500 supercomputers. Job schedulers enable large numbers of users to fairly and efficiently share large computational resources.

Cluster prerequisites

Before being able to take advantage of our computational resources, you must first set up your environment. This is pretty straightforward, but there are a few steps:

SSH access setup

You need to have your SSH keys set up to access cluster resources. If you haven't done this already, please set up your ssh keys.

SCU clusters and job partitions

Available SCU HPC resources

The SCU uses Slurm to manage the following resources:

General purpose cluster:

• The panda cluster (72 nodes): 70 CPU-only cluster intended for general use, 2 GPU (V100) nodes

CyroEM cluster: 

• The cryoEM cluster (18 nodes): 15 CPU-only nodes, 4 GPU (P100) nodes. Available only for analysis of cryoEM data

PI-specific clusters:

• The Edison cluster (9 GPU nodes):  5 k40m and 4 k80 nodes reserved for the H. Weinstein lab
• node178: GPU (p100) node reserved for the Accardi and Huang labs
• node179: GPU (p100) node reserved for the Boudker lab
• node18[4-5] (2 nodes): GPU (V100) nodes reserved for the Boudker lab
• cantley-node0[1-2] (2 nodes): GPU (V100) nodes reserved for the Cantley lab 

All jobs, except those submitted the Edison cluster, should be submitted via our Slurm submission node: curie.pbtech. Jobs submitted to the Edison cluster should be submitted from its submission node, edison-mgmt.pbtech.

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What is Slurm?

Slurm (previously Simple Linux Utility for Resource Management), is a modern, open source job scheduler that is highly scaleable and customizable; currently, Slurm is implemented on the majority of the TOP500 supercomputers. Job schedulers enable large numbers of users to fairly and efficiently share large computational resources.

Please see About SCU for more information about our HPC infrastructure

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Slurm partitions

Slurm groups nodes into sets referred to as 'partitions'. The above resources belong to one or more Slurm partitions, with each partition possessing its own unique job submission rules. Some nodes belong to multiple partitions because this affords the SCU the configurational flexibility needed to ensure fair allocation of managed resources.

Panda cluster partitions:

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Slurm partitions -  BRB Cluster 

BRB

SCU cluster partitions:

• scu-cpu: 22 cpu nodes, 7-day runtime limit
• scu-gpu: 6 gpu nodes, 2-day runtime limit

CryoEM partitions:

• cryo-cpu: 14 CPU-only nodes, 7-day runtime limit
• cryo-gpu: 6 GPU nodes, 2-day runtime limit
• cryo-gpu-v100: 2 GPU, 2-day runtime limit
• cryo-gpu-p100: 3 GPU, 2-day runtime limit

CryoEM cluster:

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PI-specific cluster partitions:

• accardi_gpu: 4 GPU nodes, 2-day runtime lim
• accardi_cpu: 1 CPU node, 7-day runtime limit
• cryo-gpu 4

  boudker_gpu: 2 GPU nodes

  (P100)

  ,

   2

   7-day runtime limit

Edison cluster:

• edison: 9 boudker_gpu-p100: 3 GPU nodes, 2 7-day runtime limitedison
• boudker_k40m: 5 GPU (k40m) cpu: 2 CPU nodes, 2 7-day runtime limit
• edisonsackler_ k80: 4 GPU (k80) nodes, 2cpu: 1 CPU node, 7-day runtime limit

PI-specific cluster partitions:

• accardi_huang_reserve: node178, sackler_ gpu: 1 GPU node, 7-day runtime limitboudker
• hwlab-rocky_reservegpu:  node179, GPU (P100) node12 GPU nodes, 7-day runtime limitboudker_reserve_v100: node18[4-5], GPU (V100)
• node, eliezer-gpu: 1 GPU node, 7-day runtime limitcantley-gpu: 2 GPU (V100) nodes, 

Other specific cluster partitions:

• scu-res: 1 GPU, 7-day runtime limit

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Of course, the above will be updated as needed; regardless, to see an up-to-date description of all available partitions, using the command sinfo on curie scu-login02. For a description of all the nodes' # CPU cores, memory (in Mb), runtime limits, and partition, use this command:

sinfo -N -o "%25N %5c %10m %15l %25R"

Or if you just want to see a description of the nodes in a given partition:

sinfo -N -o "%25N %5c %10m %15l %25R" -p panda # for the partition panda

Interactive Session

srun -n1 --pty --partition=scu-cpu --mem=8G bash -i

To request specific numbers of GPUs, you should add your request to your srun/sbatch:  

Below is an example of requesting 1 GPU - can request up to 4 GPUs on a single node

--gres=gpu:1

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A simple job submission example

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#! /bin/bash -l

#SBATCH --partition=pandascu-cpu   # cluster-specific 
#SBATCH --nodes=1
#SBATCH --ntasks=1 
#SBATCH --job-name=hello_slurm
#SBATCH --time=00:02:00   # HH/MM/SS
#SBATCH --mem=1G   # memory requested, units available: K,M,G,T
#SBATCH --output hello_slurm-%j.out
#SBATCH --error hello_slurm-%j.err

source ~/.bashrc

echo "Starting at:" `date` >> hello_slurm_output.txt
sleep 30
echo "This is job #:" $SLURM_JOB_ID >> hello_slurm_output.txt
echo "Running on node:" `hostname` >> hello_slurm_output.txt
echo "Running on cluster:" $SLURM_CLUSTER_NAME >> hello_slurm_output.txt
echo "This job was assigned the temporary (local) directory:" $TMPDIR >> hello_slurm_output.txt

exit

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Next, there are several #SBATCH lines. These lines describe the resource allocation we wish to request:

--partition=pandascu-cpu:

Cluster resources (such as specific nodes, CPUs, memory, GPUs, etc) can be assigned to groups, called partitions. Additionally, the same resources (e.g. a specific node) may belong to multiple cluster partitions. Finally, partitions may be assigned different job priority weights, so that jobs in one partition move through the job queue more quickly than jobs in another partition.

Every job submission script must request a specific partition--otherwise, the default is used. To see what partitions are available on your cluster, click here, or execute the command: sinfo

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ssh -X pascal
 
ssh -X curiecwid@scu-login01.med.cornell.edu
 
srun --x11 -n1 --pty --partition=pandascu-cpu --mem=8G bash -i

To test the session, try the following command:

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