Strtrim(evalc('system(''hostname'') '))) Ī = reshape( linspace( 1,NA, NA), size_A ) ī = reshape( linspace(NB, 1, NB), size_B ) įprintf('serial computation of matrix-matrix product: ') įunction mcr_build(srcpath, mainfunc, threadingopt) computation time (matrix product only)Įrror('Invalid number of input arguments!') Įrror(sprintf('Dimension mismatch of A (%d columns) and B (%d rows)!'.įprintf('Hello from MATLAB process PID=%d running on node %s! '. 2-element row vectors defining sizes of A and B % -> matrix-matrix multiplication C = A*B

Plot_save_matrix(C, ) įprintf(fp, '(rank=%d) computation time = %.4f s. ', myrank, comptime) įunction = matmul_serial(size_A, size_B) % get sizes of input matrices for computation of C = A*B from commandline % Scale result by taskID to show MPMD effect. įprintf(fp, 'MATLAB temporary directory = %s ', tempdir) įprintf(fp, 'MCR_CACHE_ROOT = %s ', getenv('MCR_CACHE_ROOT')) įprintf(fp, 'MCR root dir = %s ', ctfroot) % Show some information, also useful for troubleshootingįprintf(fp, 'MPI process rank=%d: I run on compute node %s and will handle task %d. '. % Create a log file for this task to avoid cluttered output of all MPI tasks inįp = fopen(sprintf('JOB_%s_TaskID_%d.log', getenv('SLURM_JOB_ID'), myrank), 'w') % Get my MPI rank from the MPI environment. % Each process will use the MPI rank as task identifier. % Each MPI process will run this code by evaluating its own rank. % threading support) as dependency 1 and a plotting function as dependency 2. % This function calls matmul_serial.m (matrix-matrix-multiplication with % - calls the Matlab function implemented by the user

% - summarizes some configuration steps, e. % This function will be compiled by Matlab Compiler. The following table depicts the procedure from creating the standalone application to the submission of a Slurm job.

The compilation of the application's MATLAB script(s) must be feasible.

That is, the work can be divided into independent subtasks.

independent processing of large datasets which are split into subsets.Įxample: Basic Parallel MPMD Application with MATLAB MCR Prerequisites.

So-called MPMD (Multiple-Program Multiple-Data) or job-farming techniques are used to run many processes of a program simultaneously and independently, that is, without the need of communication. MCR-based standalones may run embarrassingly parallel. On LRZ systems we provide all MCR versions which correspond to the installed MATLAB versions. In contrast, the standalone application should be executable on any system providing a MCR installation, and without the need of a MATLAB license. The standalone application is compiled by the MATLAB Compiler which requires a licensed version of MATLAB.

The purpose of the MATLAB Compiler Runtime is to run MATLAB standalone applications on systems without a MATLAB installation.

Matlab runtime 9