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NWZPHI the cluster of the IVV 4

NWZPHI is a cluster equipped with 98 Xeon Phi cards. These are PCIe based accelerators similar to GPUs, but can be used with regular programming languages.

Update: New Centos 7 Installation

Hard- and Software overview

  • 2 developing and debugging servers (24 CPU cores with 2.4 GHz, 64 GB RAM, 1 Xeon Phi 5110p)
  • 12 accelerator nodes (24 cores with 2.4 GHz, 128 GB RAM, 8 Xeon Phi 5110p)
  • 1 SMP node (32 CPU cores, 1.5 TB RAM)
  • 88 TB storage (with FhGFS) for home and scratch
  • FDR Infinibad as interconnect
  • The operating system is RedHat Enterprise Linux 6

NWZPHI for the impatient reader

The name of the login-server is NWZPHI. Allowed are all users that are members of the group u0clustr and at least one of the groups starting with p0, q0 or r0. In addition, every user allowed for PALMA may use NWZPHI. You can register yourself for u0clstr at MeinZIV (go to “Username (account) and group memberships” / „Nutzerkennung und Gruppenmitgliedschaften“).

The batch and module system are working very similar to PALMA.

Differences to PALMA

If you are familiar with PALMA, starting jobs on NWZPHI is quite easy. There are some differences mentioned here

  • In the submit file, you do not need the switch "-A"
  • One node has 24 CPU cores
  • The node names and properties are different
  • The operating system has another version, so you have to recompile your code
  • To use the Xeon Phi accelerators, more work is necessary (see below)

Starting jobs on NWZPHI

  • Choose your software environment and (optionally) compile your code
  • Submit your job via the batch system

Environment Modules

Environment variables (like PATH, LD_LIBRARY_PATH) for compilers and libraries can be set by modules:

Command (Short- and Long-form) Meaning
module av[ailable] Lists all available modules
module li[st] Lists all modules in the actual enviroment
module show modulname Lists all changes caused by a module
module add modul1 modul2 ... Adds module to the actual environment
module rm modul1 modul2 ... Deletes module from the actual environment
module purge Deletes all modules from actual environment
To use the same modules at every login, put the commands in your $HOME/.bashrc. The recommended default module is

module add intel/2016a
This is a toolchain that loads other modules like (Intel-) MPI and the MKL.

Batch system

The batch system Torque and the scheduler Moab are used to submit jobs. It is not allowed, to start jobs manually. Batch jobs should only be submitted from the server mn02.

Creating submit-files

Example of a submit-file of a MPI-job:

#PBS -o output.dat
#PBS -l walltime=01:00:00,nodes=2:ppn=24
#PBS -M username@uni-muenster.de
#PBS -m ae
#PBS -q default
#PBS -N job_name
#PBS -j oe
mpdboot  -n 2 -f $PBS_NODEFILE  -v
mpirun -machinefile $PBS_NODEFILE -np 48 ./executable

An MPI-job with 48 processes is started.

Further Information:

  • username: Replace by own username
  • job_directory: Replace by the path, where the executable can be found
  • executable: Enter the name of the executable
  • walltime: The time needed for a whole run. At the moment, maximal 48 hours are possible

When no MPI is needed, the submit-file can be simpler.

Example for a job using openMP:

#PBS -o output.dat
#PBS -l walltime=01:00:00,nodes=1:ppn=24
#PBS -M username@uni-muenster.de
#PBS -m ae
#PBS -q default
#PBS -N job_name
#PBS -j oe

Choosing the nodes

The cluster consists of the following nodes:

Name Hardware Queue Annotations Max Walltime
sl250-01, sl250-02 24 cores, 64 GB RAM, 1 Xeon Phi accelerator debug Debugging node, short maximal walltime, so you have less waiting time 4 hours
sl270-01-12 24 cores, 128 GB RAM, 8 Xeon Phi accelerators default Production nodes 48 hours
dl560-01 32 cores, 1,5 TB RAM bigsmp For large OpenMP computations with very high memory demands 200 hours
sl230-01-03 24 cores, 64 GB RAM p0doltsi Reserved for the Doltsinis group 160 hours
To choose the node type that you want to use, you have to use the correct queue. So if you want to run a large computation which needs more than 128 GB of RAM for a single process, the dl560 is right for you. In this case, you have to use the bigsmp queue:

#PBS -q bigsmp

Submitting jobs / Managing the queue

A job is submitted by entering the command

 qsub submit.cmd 

, where submit.cmd is the name of the submit-file.

Further commands:

  • qstat: Shows the current queue
  • qstat -a: As above, but with the number of requested cores
  • qstat -n: Shows in detail, which nodes are used
  • qdel job_number: Deletes jobs from the queue
  • showbf: Shows the number of free cores

Monitoring jobs

There are different tools for monitoring

  • qstat -a: Shows the queues with running and waiting jobs
  • pbstop: Similar to qstat but with a text-based graphical output
  • Ganglia: Shows detailed information of every node including memory and CPU usage


There is a 88 TB partition for /home and /scratch using the BeeGFS filesystem (formerly known as FHGfs). Try to store your data like on PALMA: Put your programs in /home and your data in /scratch. Due to the amount of data, there is no backup at the moment.

Using the Xeon Phi accelerators

If you want to use the accelerators, you always have to reserver three times more CPU cores than accelerators like:

qsub -I -q default -l nodes=1:ppn=9:mics=3

You have to recompile your code (with the Intel compiler) with the "-mmic" Flag, so you have to create a separate version for the host and the accelerator.

mpiicpc code.c -mmic -o program.mic

To use the accelerators that have been reserved for you, you can use the script "allocated-mics.pl" which is in your PATH. The host names of the cards are no longer mic0, mic1..., but have the name of their hosts in it so this would be sl270-01-mic0, sl270-01-mic1 and so on. This is necessary to set up the communication between the accelerators.

An example how to use the accelerators with MPI could look like this:

allocated-mics.pl > ${HOME}/mics.list
mpirun -n 120 -hostfile ${HOME}/mics.list ./program.mic

Each card has 60 cores and can have up to four threads per core, so all in all 240 threads per card can be created. But from my experience it might be better to use only 120 threads per card.

Some useful links

Known Issues

For temporary problems please read the login messages.

Other problems are:

  • No communication between Xeon Phi accelerators of different nodes is possible at the moment This has been fixed with the reinstallation with CentOS7


In case of questions, please ask Holger Angenent or Martin Leweling via hpc@uni-muenster.de.

-- HolgerAngenent - 2014-07-23

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Topic revision: r11 - 2017-12-13 - HolgerAngenent
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