How to get your MATLAB work done on NeSI's Pan cluster

1. Prerequisites: Work through the companion document OtagoPanInstructions first.
2. Type nano simplefunction.m and Copy and Paste the following (it makes a random matrix and displays the result of a calculation), write out and exit:

   
   function simplefunction(n)
   n = str2double(n);
   a = rand(n);
   save simpleMatlab a
   2^10
   
   

3. Type nano simpleMatlab.sl and Copy and Paste the following (you need to replace projectIDhere with your project ID from OtagoPanInstructions), write out and exit:

   
   #!/bin/bash
   #SBATCH -J yourjobName
   #SBATCH -A projectIDhere
   #SBATCH --time=1:00:00
   #SBATCH --mem-per-cpu=2048
   #SBATCH --output=simpleMatlabout.txt
   #SBATCH --error=simpleMatlaberr.txt
   module load MATLAB/2015b
   srun matlab -nodesktop -nosplash -nodisplay -nojvm -r 'simplefunction 20'
   
   

4. Submit the Slurm script you created in step 2 by typing: sbatch simpleMatlab.sl.
5. When your job is finished, you should find three new files:
   • simpleMatlab.mat: a MATLAB file with a 20 by 20 random matrix in it
   • simpleMatlabout.txt: including the text "1024" from calculating 2^10
   • simpleMatlaberr.txt: empty file
6. You copy the file simpleMatlab.mat to your local machine (see OtagoPanInstructions) or look at it on the build node using:

   ssh build-sb 
   module load MATLAB/2015b
   matlab
   load simpleMatlab
   disp(a)
   
   

7. Changing your code to work on Pan
   • Inputs: The code you want to run will be a MATLAB script or function. If it is a script then it has no direct inputs (perhaps it will load a .mat file, or all parameters will be set in the script). If it is a function then inputs will be received by MATLAB as text strings. If you need numeric inputs, you'll need to use e.g. str2double (as we did in step 2 above).
   • Outputs: It is not useful for your function to return explicit outputs: either use the text output (what you normally see on the screen which goes into the file you specify with #SBATCH --output=) or (much more likely) save results, e.g. to a .mat file (as we did in step 1).
   • To use more than one core, add the following to your .sl script (change the 8 to the number of CPUs your code can use):

     
     #SBATCH --cpus-per-task=8
     #SBATCH --ntasks=1