#!/bin/bash --login
#This example use general SBATCH settings, but please refer to the specific guide
#of intended the cluster for possible needed changes

#
# SLURM directives
#
# This is an array job with 35 subtasks, (--array=0-34).
#
# The output for each subtask will be sent to a separate file
# identified by the jobid (--output=array-%j.out)
# 
# Each subtask will occupy one node (--nodes=1) with
# a wall-clock time limit of twenty minutes (--time=00:20:00)
#
# Replace [your-project] with the appropriate project name
# following --account (e.g., --account=project123)


#SBATCH --account=[your-project]
#SBATCH --output=array-%j.out
#SBATCH --array=0-34       #this should match the number of input files
#SBATCH --nodes=1
#SBATCH --ntasks=1          #One main task per file to be processed in each array-subtask
#SBATCH --cpus-per-task=1 	#this will vary depending on the requirements of the task
#SBATCH --mem=1840M         #Needed memory per array-subtask (or use --exclusive for exclusive access)
#SBATCH --time=00:20:00

#---  
echo "All jobs in this array have:"
echo "- SLURM_ARRAY_JOB_ID=${SLURM_ARRAY_JOB_ID}"
echo "- SLURM_ARRAY_TASK_COUNT=${SLURM_ARRAY_TASK_COUNT}"
echo "- SLURM_ARRAY_TASK_MIN=${SLURM_ARRAY_TASK_MIN}"
echo "- SLURM_ARRAY_TASK_MAX=${SLURM_ARRAY_TASK_MAX}"
 
echo "This job in the array has:"
echo "- SLURM_JOB_ID=${SLURM_JOB_ID}"
echo "- SLURM_ARRAY_TASK_ID=${SLURM_ARRAY_TASK_ID}"

#--- 
#Specific settings for the cluster you are on
#(Check the specific guide of the cluster for additional settings)

#---  
# grab our filename from a directory listing
FILES=($(ls -1 *.input.txt)) #this pulls in all the files ending with input.txt
FILENAME=${FILES[$SLURM_ARRAY_TASK_ID]} #this allows the slurm to enter the input.txt files into the job array
echo "My input file is ${FILENAME}" #this will print the file name into the log file 

#---  
#example job using the above variables
srun -N 1 -n 1 -c 1 ExpansionHunterDenovo-v0.8.7-linux_x86_64/scripts/casecontrol.py locus \
        --manifest ${FILENAME} \
        --output ${FILENAME}.CC_locus.tsv

$ squeue -u espinosamatilda
JOBID    USER     ACCOUNT                   NAME EXEC_HOST ST     REASON   START_TIME     END_TIME  TIME_LEFT NODES   PRIORITY
3483798  espn   matilda  pawsey            iterativeJob  nid00017  R       None     14:44:27     14:47:27       2:50     1       5269
3483799  espn   matilda  pawsey            iterativeJob       n/a PD Dependency          N/A          N/A       5:00     1       5269

#!/bin/bash --login


#SBATCH --account=[your-account]-gpu
#SBATCH --partition=nvlinkqgpu
#SBATCH --nodes=1
#SBATCH --ntasks=4
#SBATCH --ntasks-per-socket=22   #maximum 2 tasks per socket (each socket has 2 GPUs in this partition)
#SBATCH --cpus-per-task=1
#SBATCH --gres=gpu:4
#SBATCH --time=00:10:00
 
#Default loaded compiler module is gcc module
 
module load cuda

for tagID in $(seq 0 3); do
   #Go to the right directory for this step of the job pack using tagID as the identifier:
   #We are assuming all the input files needed for each specific job reside in the corresponding working directory
   cd ${SLURM_SUBMIT_DIR}/workingDir_${tagID}

   #Defining an output file for this step
   outputFile=results_${tagID}.out
   echo "Starting" > $outputFile

   #Run the cuda executable (asuming the same executable will be used by each step, and that it resides in the submission directory):
   srun -u -N 1 -n 1 --mem=056G --gres=gpu:1 --exact ${SLURM_SUBMIT_DIR}/main_cuda >> $outputFile &
done
wait

• In the header a total of four GPUS is requested. For each job step the specific number of GPUs to be used (1 in this case) is indicated. The use of --mem=0 is to avoid memory restrictions56G indicates the amount of memory to be allocated for each step, and the --exact option avoids possible sharing of allows access to only the resources requested for that specific the step.
• Note the logic of the use of " & .. & ..wait" for being able to execute each step in the background and wait for them to finish before ending the job script.
• In the loop, the iterator (numeric identifier) for each step is defined to start at 0 in order to be equivalent to the natural numbering of Slurm, but you can use any start and end value to be consistent with your own naming of directories, input files and output files.

#!/bin/bash --login

#SBATCH --account=[your-account]
#SBATCH --partition=nvlinkq
#SBATCH --nodes=1
#SBATCH --ntasks=4
#SBATCH --ntasks-per-socket=2    #maximum 2 tasks per socket (each socket has 2 GPUs in this partition) #SBATCH --cpus-pert-task=1
#SBATCH --gres=gpu:4
#SBATCH --gpu-bind=map_gpu:0,1,2,3
#SBATCH --time=00:10:00
#SBATCH
--account=[your-account] #SBATCH
--export=NONE
 
#Default loaded compiler module is gcc module
 
module load cuda

#Run the cuda executable from a wrapper:
srun -u -N 1 -n 4 -c 1 wrapper.sh