...

>> % If state is finished, fetch results

>> j.fetchOutputs{:}

>> % Display the diary

diary(j)

>> % Delete the job after results are no longer needed

...

CIN_EXAMPLE=/cineca/prod/build/tools/matlab/CINECA_example

Example 1: parallel_example.m

Example 2: hpccLinpack.m

Example 1 

parallel_example.m

 Let’s use the following example for a parallel job. 

...

>> j.fetchOutputs{:}

ans = 15.5328

>> % Display the diary

diary(j)

The job ran in 15.53 sec. using 4 workers. 

...

The job now runs 6.4488 seconds using 8 workers.  Run code with different number of workers to determine the ideal number to use.

Example 2

hpccLinpack.m

This example is taken from $MATLAB_HOME/toolbox/distcomp/examples/benchmark/hpcchallenge/ 

It is an implementation of the HPCC Global HPL benchmark

function perf = hpccLinpack( m )

The function input is the size of the real matrix m-by-m to be inverted. The outputs is  perf, performance in gigaflops

Start to submit on 1 core, with m=1024: 

j = c.batch(@hpccLinpack, 1, {1024}, 'Pool', 1)

Data size: 0.007812 GB
Performance: 1.576476 GFlops

Repeat on one full node 

j = c.batch(@hpccLinpack, 1, {1024}, 'Pool', 35)

Data size: 0.007812 GB
Performance: 0.311111 GFlops

Increase the size of the matrix, 

 j = c.batch(@hpccLinpack, 1, {2048}, 'Pool', 35)

Increase the number of nodes, and so on....

MdcsDataLocation

Please take into account that the info and the metadatas on your jobs are stored in your $HOME directory under: 

$HOME/MdcsDataLocation/cineca/R2018a/local

Please check the disk quota of this directory and remove old/unused metadatas.

Debugging

If a serial job produces an error, we can call the getDebugLog method to view the error log file.

...