Matlab-flags changed

Dear Users,

since we observe a considerable amount of Matlab-Jobs in the cluster, and observe some troubles in context with the Batch-System, we have made some changes on the matlab startup-script.

Before talking about the changes we want to remind you that your scripts should be compiled using the Matlab-Compiler since we have only a few licenses in the cluster and those are meant to be used for interactive use, mainly (code-checking, profiling,...).

One major issue that repeatedly is coming up is a mismatch of ressource reservation. Matlab tries to use all hardware on a machine by default. This might be fine on your local workstation but it's not on a cluster. As for Matlab there's just the option of taking one core or taking them all. This can be controlled by the use of the flag -singleCompThread. It seems like not many users are aware of this, which made us changing the startup to use this flag -singleCompThread by default. If you have a script that takes advantage of internal, parallelized routines of Matlab and want to allow it to take the full machine, you have to use the new flag -multiCompThread and reserve a full node, of coure.

Let's look at two examples how to start Matlab, just to elaborate it:

1.) Matlab-Script that needs only one computational thread

matlab -nosplash -r my_singleComp_script

2.) Matlab-Script that makes use of a lot of internal, parallelized routines (only inside an interactive job):

matlab -nosplash -multiCompThread -r my_multiComp_script

As mentioned above, the code should be compiled when run in the cluster as a job. Accordingly, the compiling should look like:

1.) mcc -R -m my_singleComp_script or
2.) mcc -R -multiCompThread -m my_multiComp_script

Note that it's NOT '-R -multiCompThread' but just '-multiCompThread'.

Your job reservation has to be adapted to the needs of your script, of course.

We have added the environment variable MATLABROOT to the module files to make the call of your compiled code a little more convenient:
./run_myCompiledCode.sh $MATLABROOT arg1 arg2 arg3 ...

In case you have a strong use of the Floating-Point-Unit you might consider using -R 'affinity[core(2)]' or more advanced reservations.

Your HPC-Team

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New OpenMPI-Module(s) supporting MPI 3.1 standard

Dear Users,

We are pleased to announce the installation of new OpenMPI (version 1.10.2) modules, supporting the MPI 3.1 standard. Please check out the respective site in our wiki.

For Fortran users: Please note the different compiler versions - they have to match the compiler version noted in the module string. All other software can be compiled against the default module with the systems compiler (gcc 4.4.7). The wiki gives a more detailed description.

Your HPC-Team

Using mogonfs

Dear Users,

We occasionally mentioned that file transfers can be a lot faster, if no encryption (like with scp ) is applied. And for quite a while the wiki stated example will follow asap in the section on ftp. Well, a comprehensive introduction to (l)ftp is not our goal, but at least know we have the promised example.

Your HPC-Team

„Going Productive“

Dear Users,


Noticed a low turnover of your jobs? One potential - and alas not so infrequent - cause is taking too much resources.

Ok, what is "too much" with regard to resources (CPU time, RAM)? Of course, you do not want to see your jobs crashing because they hit the run time or memory limit. Therefore you rather ask for a little overhead. And this is what we recommend as well. After all: What is the point if you loose time and have to re-submit?

Yet, asking for 3 GB, when the first 1000 jobs took all below 0.5 GB will cause you to occupy slots where other users and also your jobs could be running. This assumes jobs, of course, with only one or a few slots. If taking multiple nodes, you will be unnecessarily waiting for nodes with more memory. (We sometimes observe memory requirement ratios which are worse.)

Likewise for run time limits: Always asking for the maximum run time of a queue, will impair backfilling, the mechanism which attempts to use all potential CPU time. As a consequence your jobs will be pending longer than needed.

So, instead of stuffing queues with untested jobs, we ask you to test a few jobs (which might require some great overhead in terms of run time and memory). Look for the actual run time in the LSF report and also the actual memory used (its maximum value). It is then still alright to "round up" those values, just to be safe. However, try not to be too cautious as a "too much" will result in a slower work flow for you.

We reserve the right to point you to problematic usage. But remember: We offer courses, individual counseling, etc.. Just ask for our help, if in doubt.

Your HPC team


Dear Users,

We frequently see jobs dying because of faulty scripts. This is part of a development cycle. After all: Who is perfect?

There are other ways than trying to correct the script post mortem - and saving time. One is to just check the syntax without executing a script:

Another, very powerful tool is this on-line shell checker tool.

Your HPC-Team


Customizing bqueues

Many of you have noticed the increasing number of queues. This can be confusing when you just want to check the current status of the queues you're allowed to use. But there is an easy way to change this. You can tell bqueues to show just "your" queues by calling it using

If you want bqueues to always show your queues only, you can just add an alias to your .bashrc (found in your home-directory). Here, is a simple command to add it to your .bashrc-file:

Now, either you call

Or you wait until your next login to Mogon and the changes will be active.

If you want to see all queues again, you have to call unalias bqueues . Then bqueues is back to "normal" for the current shell.

Your HPC Team

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New Policy: Limited number of files in home directories

Dear Users,


As consequence of the recently increased quota in mogon home directories some users have been saving an excess amount of files in their home directory (fs1).

Due to performance reasons we therefore have to limit the number of files allowed in home directories to 1 million. Policies are linked in our wiki, this one can be found here.

Thank you for your consideration.

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Python 3.5 with numpy linked against MKL

Dear Users,


We now provide a new version of Python -- 3.5-- as usual along with a great number of scientific Python modules. In particular, numpy, the basic array library, is compiled against Intel's MKL resulting in a tremendous speed-up when dealing with numerical computations, e.g. matrix multiplication.


See our wiki entry for more details.

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