Sample SLURM Scripts

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HiPerGator 2.0 documentation

Sample SLURM Scripts

Below are a number of sample scripts that can be used as a template for building your own SLURM submission scripts for use on HiPerGator 2.0. These scripts are also located at: /ufrc/data/training/SLURM/, and can be copied from there. If you choose to copy one of these sample scripts, please make sure you understand what each line of the sbatch directives before using it to submit your jobs. Otherwise, you may not get the result you want and may waste valuable computing resources.

Basic, single-processor job

This script can serve as the template for many single-processor applications. The mem-per-cpu flag can be used to request the appropriate amount of memory for your job. Please make sure to test your application and set this value to a reasonable number based on actual memory use. The %j in the -o (can also use --output) line tells SLURM to substitute the job ID in the name of the output file. You can also add a -e or --error with an error file name to separate output and error logs.

Download the single_processor_job.sh script

#!/bin/sh
#SBATCH --job-name=serial_job_test    # Job name
#SBATCH --mail-type=ALL               # Mail events (NONE, BEGIN, END, FAIL, ALL)
#SBATCH --mail-user=<email_address>   # Where to send mail	
#SBATCH --ntasks=1                    # Run on a single CPU
#SBATCH --mem=600mb                   # Memory limit
#SBATCH --time=00:05:00               # Time limit hrs:min:sec
#SBATCH --output=serial_test_%j.out   # Standard output and error log
 
pwd; hostname; date
 
module load python
 
echo "Running plot script on a single CPU core"
 
python /ufrc/data/training/SLURM/plot_template.py
 
date

Threaded or multi-processor job

This script can serve as a template for applications that are capable of using multiple processors on a single server or physical computer. These applications are commonly referred to as threaded, OpenMP, PTHREADS, or shared memory applications. While they can use multiple processors, they cannot make use of multiple servers and all the processors must be on the same node.

These applications required shared memory and can only run on one node; as such it is important to remember the following:

  • You must set --nodes=1, and then set --cpus-per-task to the number of OpenMP threads you wish to use.
  • You must make the application aware of how many processors to use. How that is done depends on the application:
    • For some applications, set OMP_NUM_THREADS to a value less than or equal to the number of cpus-per-task you set.
    • For some applications, use a command line option when calling that application.


Download the multi_processor_job.sh script

#!/bin/sh
#SBATCH --job-name=parallel_job_test # Job name
#SBATCH --mail-type=ALL              # Mail events (NONE, BEGIN, END, FAIL, ALL)
#SBATCH --mail-user=<email_address>  # Where to send mail	
#SBATCH --nodes=1                    # Use one node
#SBATCH --ntasks=1                   # Run a single task	
#SBATCH --cpus-per-task=4            # Number of CPU cores per task
#SBATCH --mem=600mb                  # Total memory limit
#SBATCH --time=00:05:00              # Time limit hrs:min:sec
#SBATCH --output=parallel_%j.out     # Standard output and error log
 
pwd; hostname; date
 
echo "Running prime number generator program on $SLURM_CPUS_ON_NODE CPU cores"
 
module load gcc/5.2.0 
 
/ufrc/data/training/SLURM/prime/prime
 
date


Another example, setting OMP_NUM_THREADS:

Download the multi_processor_job2.sh script

#!/bin/sh
#SBATCH --job-name=parallel_job_test # Job name
#SBATCH --mail-type=ALL              # Mail events (NONE, BEGIN, END, FAIL, ALL)
#SBATCH --mail-user=<email_address>  # Where to send mail	
#SBATCH --nodes=1                    # Use one node
#SBATCH --ntasks=1                   # Run a single task	
#SBATCH --cpus-per-task=4            # Number of CPU cores per task
#SBATCH --mem=600mb                  # Total memory limit
#SBATCH --time=00:05:00              # Time limit hrs:min:sec
#SBATCH --output=parallel_%j.out     # Standard output and error log
 
export OMP_NUM_THREADS=4
 
# Load required modules; for example, if your program was
# compiled with Intel compiler, use the following 
module load intel
 
# Run your program with correct path and command line options
./YOURPROGRAM INPUT

MPI job

This script can serve as a template for MPI, or message passing interface, applications. These are applications that can use multiple processors that may, or may not, be on multiple servers.

Our testing has found that it is best to be very specific about how you want your MPI ranks laid out across nodes and even sockets (multi-core CPUs). SLURM and OpenMPI have some conflicting behavior if you leave too much to chance. Please refer to the full SLURM sbatch documentation, but the following directives are the main directives to pay attention to:

  • -c, --cpus-per-task=<ncpus>
    • Advise the Slurm controller that ensuing job steps will require ncpus number of processors per task.
  • -m, --distribution=arbitrary|<block|cyclic|plane=<options>[:block|cyclic|fcyclic]>
    • Specify alternate distribution methods for remote processes.
    • We recommend -m cyclic:cyclic, which tells SLURM to distribute tasks cyclically over nodes and sockets.
  • -N, --nodes=<minnodes[-maxnodes]>
    • Request that a minimum of minnodes nodes be allocated to this job.
  • -n, --ntasks=<number>
    • Number of tasks (MPI ranks)
  • --ntasks-per-node=<ntasks>
    • Request that ntasks be invoked on each node
  • --ntasks-per-socket=<ntasks>
    • Request the maximum ntasks be invoked on each socket
    • Notes on socket layout:
      • hpg2-compute nodes have 2 sockets, each with 16 cores.
      • hpg1-compute nodes have 4 sockets, each with 16 cores.


The following example requests 24 tasks, each with one core. It further specifies that these should be split evenly on 2 nodes, and within the nodes, the 12 tasks should be evenly split on the two sockets. So each CPU on the two nodes will have 6 tasks, each with its own dedicated core. The distribution option will ensure that MPI ranks are distributed cyclically on nodes and sockets.

SLURM is very flexible and allows users to be very specific about their resource requests. Thinking about your application and doing some testing will be important to determine the best request for your specific use.

Download the mpi_job.sh script

#!/bin/sh
#SBATCH --job-name=mpi_job_test      # Job name
#SBATCH --mail-type=ALL              # Mail events (NONE, BEGIN, END, FAIL, ALL)
#SBATCH --mail-user=<email_address>  # Where to send mail	
#SBATCH --ntasks=24                  # Number of MPI ranks
#SBATCH --cpus-per-task=1            # Number of cores per MPI rank 
#SBATCH --nodes=2                    # Number of nodes
#SBATCH --ntasks-per-node=12         # How many tasks on each node
#SBATCH --ntasks-per-socket=6        # How many tasks on each CPU or socket
#SBATCH --distribution=cyclic:cyclic # Distribute tasks cyclically on nodes and sockets
#SBATCH --mem-per-cpu=600mb          # Memory per processor
#SBATCH --time=00:05:00              # Time limit hrs:min:sec
#SBATCH --output=mpi_test_%j.out     # Standard output and error log
pwd; hostname; date
 
echo "Running prime number generator program on $SLURM_JOB_NUM_NODES nodes with $SLURM_NTASKS tasks, each with $SLURM_CPUS_PER_TASK cores."
 
module load intel/2016.0.109 openmpi/1.10.2
 
srun --mpi=pmi2 /ufrc/data/training/SLURM/prime/prime_mpi
 
date

Hybrid MPI/Threaded job

This script can serve as a template for hybrid MPI/Threaded applications. These are MPI applications where each MPI rank is threaded and can use multiple processors.

Our testing has found that it is best to be very specific about how you want your MPI ranks laid out across nodes and even sockets (multi-core CPUs). SLURM and OpenMPI have some conflicting behavior if you leave too much to chance. Please refer to the full SLURM sbatch documentation, as well as the information in the MPI example above.

The following example requests 8 tasks, each with 4 cores. It further specifies that these should be split evenly on 2 nodes, and within the nodes, the 4 tasks should be evenly split on the two sockets. So each CPU on the two nodes will have 2 tasks, each with 4 cores. The distribution option will ensure that MPI ranks are distributed cyclically on nodes and sockets.

Download the hybrid_pthreads_job.sh script

#!/bin/sh
#SBATCH --job-name=hybrid_job_test      # Job name
#SBATCH --mail-type=ALL                 # Mail events (NONE, BEGIN, END, FAIL, ALL)
#SBATCH --mail-user=<email_address>     # Where to send mail	
#SBATCH --ntasks=8                      # Number of MPI ranks
#SBATCH --cpus-per-task=4               # Number of cores per MPI rank 
#SBATCH --nodes=2                       # Number of nodes
#SBATCH --ntasks-per-node=4             # How many tasks on each node
#SBATCH --ntasks-per-socket=2           # How many tasks on each CPU or socket
#SBATCH --mem-per-cpu=100mb             # Memory per core
#SBATCH --time=00:05:00                 # Time limit hrs:min:sec
#SBATCH --output=hybrid_test_%j.out     # Standard output and error log
 
pwd; hostname; date
 
module load intel/2016.0.109 openmpi/1.10.2 raxml/8.2.8
 
srun --mpi=pmi2 raxmlHPC-HYBRID-SSE3 -T $SLURM_CPUS_PER_TASK \
      -f a -m GTRGAMMA -s /ufrc/data/training/SLURM/dna.phy -p $RANDOM \
      -x $RANDOM -N 500 -n dna
 
date

The following example requests 8 tasks, each with 8 cores. It further specifies that these should be split evenly on 4 nodes, and within the nodes, the 2 tasks should be split, one on each of the two sockets. So each CPU on the two nodes will have 1 task, each with 8 cores. The distribution option will ensure that MPI ranks are distributed cyclically on nodes and sockets.

Also note setting OMP_NUM_THREADS so that OpenMP knows how many threads to use per task.

Download the hybrid_OpenMP_job.sh script

#!/bin/bash
 
#SBATCH --job-name=LAMMPS
#SBATCH --output=LAMMPS_%j.out
#SBATCH --mail-type=ALL
#SBATCH --mail-user=<email_address>
#SBATCH --nodes=4              # Number of nodes
#SBATCH --ntasks=8             # Number of MPI ranks
#SBATCH --ntasks-per-node=2    # Number of MPI ranks per node
#SBATCH --ntasks-per-socket=1  # Number of tasks per processor socket on the node
#SBATCH --cpus-per-task=8      # Number of OpenMP threads for each MPI process/rank
#SBATCH --mem-per-cpu=2000mb   # Per processor memory request
#SBATCH --time=4-00:00:00      # Walltime in hh:mm:ss or d-hh:mm:ss
 
date
hostname
 
module load intel/2016.0.109 openmpi/1.10.2
 
export OMP_NUM_THREADS=8
 
srun --mpi=pmi2 /path/to/app/lmp_gator2 < in.Cu.v.24nm.eq_xrd
  • Note that MPI gets -np from SLURM automatically.
  • Note there are many directives available to control processor layout.
    • Some to pay particular attention to are:
      • --nodes if you care exactly how many nodes are used
      • --ntasks-per-node to limit number of tasks on a node
      • --distribution one of several directives (see also --contiguous, --cores-per-socket, --mem_bind, --ntasks-per-socket, --sockets-per-node) to control how tasks, cores and memory are distributed among nodes, sockets and cores. While SLURM will generally make appropriate decisions for setting up jobs, careful use of these directives can significantly enhance job performance and users are encouraged to profile application performance under different conditions.

Array job

Please see the SLURM_Job_Arrays page for information on job arrays. Note that we use the simplest 'single-threaded' process example from above and extending it to an array of jobs. Modify the following script using the parallel, mpi, or hybrid job layout as needed.

Download the array_job.sh script

#!/bin/sh
#SBATCH --job-name=array_job_test   # Job name
#SBATCH --mail-type=ALL             # Mail events (NONE, BEGIN, END, FAIL, ALL)
#SBATCH --mail-user=<email_address> # Where to send mail	
#SBATCH --nodes=1                   # Use one node
#SBATCH --ntasks=1                  # Run a single task
#SBATCH --mem-per-cpu=1gb           # Memory per processor
#SBATCH --time=00:05:00             # Time limit hrs:min:sec
#SBATCH --output=array_%A-$a.out    # Standard output and error log
#SBATCH --array=1-5                 # Array range
pwd; hostname; date
 
echo This is task $SLURM_ARRAY_TASK_ID
 
date

Note the use of %A for the master job ID of the array, and the %a for the task ID in the output filename.

GPU job

Please see GPU_Access for more information regarding the use of HiPerGator GPUs.

Download the gpu_job.sh script

#!/bin/bash
#SBATCH --job-name=gpuMemTest
#SBATCH --output=gpuMemTest.out
#SBATCH --error=gpuMemTest.err
#SBATCH --ntasks=2
#SBATCH --cpus-per-task=1
#SBATCH --distribution=cyclic:cyclic
#SBATCH --time=12:00:00
#SBATCH --mem-per-cpu=2000
#SBATCH --mail-type=ALL
#SBATCH --mail-user=taylor@rc.ufl.edu
#SBATCH --account=ufhpc
#SBATCH --qos=ufhpc-b
#SBATCH --partition=hpg1-gpu
#SBATCH --gres=gpu:tesla:2
 
module load cuda/5.5
 
cudaMemTest=/home/taylor/Cuda/cudaMemTest/cuda_memtest
 
cudaDevs=$(echo $CUDA_VISIBLE_DEVICES | sed -e 's/,/ /g')
 
for cudaDev in $cudaDevs
do
  echo cudaDev = $cudaDev
  #srun --gres=gpu:tesla:1 -n 1 --exclusive ./gpuMemTest.sh > gpuMemTest.out.$cudaDev 2>&1 &
  $cudaMemTest --num_passes 1 --device $cudaDev > gpuMemTest.out.$cudaDev 2>&1 &
done
wait