>> configCluster(cluster_name, partition_name);

Please check the requirements of your job, i. e., the number of tasks (workers) and tasks per node. Then set the correct cluster and partition.

>> ch = parcluster;

>> % Job walltime in format hh:mm:ss => --time=00:30:00 in Slurm
>> ch.AdditionalProperties.WallTime = '00:30:00';

>> % MPI tasks per node => --tasks-per-node=28 in Slurm
>> ch.AdditionalProperties.ProcsPerNode = 28;		

>> % additional: disabling multi-threading and setting memory requirement
>> ch.AdditionalProperties.AdditionalSubmitArgs = '--cpus-per-task=1 --mem=55G';  

>> jobdir = fullfile(getenv('SCRATCH'), 'MdcsDataLocation/coolmuc', version('-release'));
>> if ~exist(jobdir), mkdir(jobdir); end
>> ch.JobStorageLocation = jobdir; 

>> ch.saveProfile;

>> job = ch.batch(@myfunction, n_arg_out, {arg_in_1, ..., arg_in_n}, 'Pool', np);

Input:
@myfunction ... reference to myfunction
n_arg_out ..... number of output arguments of myfunction
arg_in_# ...... list of input arguments of myfunction
'Pool', np .... key-value pair with size of parallel pool
                = number of workers
Output:
job ........... job object providing all job information and
                member functions to control the job

IMPORTANT:
The setting of np workers will result in the allocation of np+1 tasks, because MPS requires an additional management task!

Example: The job uses 14 tasks per node and 28 workers in total. Including an additional task a third compute node with only one task will be involved into the job. That results in inefficient resource usage and probably longer waiting times!

>> job.State



>> job.cancel

>> myresults = job.fetchOutputs

show current state ('queued', 'running', 'finished', 'failed'),
equivalent to "squeue --clusters=cm2 --users=$USER"
in Linux command line

cancel job, i. e. remove it from the Slurm queue

obtain all results (return values) from myfunction

The MATLAB release has to match one of the releases supported by LRZ. Please refer to next step.

Using file, showing a release mismatch between MPS and installed MATLAB, will cause a failure of MPS jobs.

> unzip matlab-RYYYYx.mps.remote.zip
> cp -r matlab-RYYYYx.mps.remote/* <MATLAB_PATH>/toolbox/local/

The zip file matlab-RYYYYx.mps.remote.zip contains the directory
matlab-RYYYYx.mps.remote. Please copy its content, not the entire directory.

MATLAB_PATH refers to the base directory of your MATLAB installation.

function [job,ch] = job_config(example, ...
                               cluster_name, ...
                               partition_name, ...
                               walltime, ...
                               tasks_per_node, ...
                               num_worker)

%===============================================================================
% MATLAB MPS EXAMPLE
% -> configuration script to initialze MPS and submit job 
%===============================================================================
% input:
% example .................... name of user function without extension ".m"
% cluster_name ............... name of cluster, e. g.: cm2 (refers to the HPC
%                              machine, e.g. CoolMUC-2)
% partition_name ............. name of queue/partition, e. g.: cm2_std
%                              Details on cluster/partition names:
%                              https://doku.lrz.de/x/AgaVAg
% walltime, tasks_per_node ... equivalent to Slurm parameters
% num_worker ................. number of MPS workers
% return values:
% job ........................ job handle
% ch ......................... cluster object handle
%===============================================================================

%===============================================================================
% Step 1: cluster configuration
%===============================================================================
configCluster(cluster_name, partition_name);

%===============================================================================
% Step 2: job configuration
%===============================================================================
ch = parcluster;
jobdir = fullfile(getenv('SCRATCH'), 'MdcsDataLocation/coolmuc/', version('-release'));
if ~exist(jobdir)
    mkdir(jobdir);
end
ch.JobStorageLocation = jobdir; 
ch.AdditionalProperties.WallTime = walltime;
ch.AdditionalProperties.ProcsPerNode = tasks_per_node;
ch.saveProfile;

%===============================================================================
% Step 3: job submission to Slurm
%===============================================================================
% Command:
%   job = ch.batch(@myfunction, n_arg_out, {arg_in_1, ..., arg_in_n}, 'Pool', np)
% Input:
%   @myfunc ...... reference to user-defined function/script myfunc.m
%   n_arg_out .... number of expected output arguments
%   {arg_in_#} ... list of input arguments of myfunction
%   'Pool', np ... key-value-pair: define size of pool (number of workers)
%
% Help via Matlab commands:
%   help batch
%   doc batch

fhandle = eval(sprintf('@%s', example));
job = ch.batch(fhandle, 4, {}, 'Pool', num_worker);

function [nlabs,comptime,Cref,Cfin] = myfunction_spmd

%===================================================================
% MATLAB EXAMPLE: PARALLEL HELLO WORLD USING PCT TOOLBOX
%                 -> matrix-matrix multiplication C = A*B
%
% return values:
% nlabs ...... total number of workers (just FYI and used by data
%              distribution functions)
% comptime ... time needed for multiplication
% Cref ....... reference result obtained from serial computation
% Cfin ....... result obtained from parallel computation
%===================================================================

%===================================================================
% Input
%===================================================================
% exemplary matrices
SIZE_A = [2000 100];
SIZE_B = [100 8000];

A = zeros(SIZE_A);
B = zeros(SIZE_B);

for n=1:SIZE_A(1)
    A(n,:) = linspace(1,n, SIZE_A(2));
end
for n=1:SIZE_B(1)
    B(n,:) = linspace(1,n, SIZE_B(2));
end

% reference result
Cref = A*B;

%===================================================================
% Manage parallel pool
%===================================================================
% get number of workers:
spmd
    nl = numlabs;
end
nlabs = nl{:};

% disallow Threading
maxNumCompThreads(1);

%===================================================================
% Parallel work
%===================================================================
SIZE_C = [SIZE_A(1) SIZE_B(2)];

% parallel environment
spmd
    % distribute data to all workers
    Ad = codistributed(A, codistributor2dbc([nlabs 1]));
    Bd = codistributed(B, codistributor2dbc([1 nlabs]));
    Cd = zeros(SIZE_C, codistributor2dbc([1 nlabs]));

    % timing
    tic;
    Cd = Ad*Bd;
    t = toc;
end

% collect data from all workers => final result
Cfin = gather(Cd);

comptime = t{:}

function [nlabs,comptime,Cref,C] = myfunc_parfor

%===================================================================
% MATLAB EXAMPLE: PARALLEL HELLO WORLD USING PCT TOOLBOX
%                 -> matrix-matrix multiplication C = A*B
%
% return values:
% nlabs ...... total number of workers (just FYI and used by data
%              distribution functions)
% comptime ... time needed for multiplication
% Cref ....... reference result obtained from serial computation
% Cfin ....... result obtained from parallel computation
%===================================================================

%===================================================================
% Input
%===================================================================
% exemplary matrices
SIZE_A = [2000 100];
SIZE_B = [100 8000];

A = zeros(SIZE_A);
B = zeros(SIZE_B);

for n=1:SIZE_A(1)
    A(n,:) = linspace(1,n, SIZE_A(2));
end
for n=1:SIZE_B(1)
    B(n,:) = linspace(1,n, SIZE_B(2));
end

% reference result
Cref = A*B;

%===================================================================
% Manage parallel pool
%===================================================================
% get number of workers:
spmd
    nl = numlabs;
end
nlabs = nl{:};

% disallow Threading
maxNumCompThreads(1);

%===================================================================
% Parallel work
%===================================================================
SIZE_C = [SIZE_A(1) SIZE_B(2)];
C = zeros(SIZE_C);

% timing
tic;
% parallel environment
parfor n=1:SIZE_A(1)
    % compute
    C(n,:) = A(n,:)*B;
end
comptime = toc;