This is an old revision of the document!

High Performance Computing

For using our compute cluster you need a GWDG account. This account is, by default, not activated for the use of the compute resources. To get it activated, please send an informal email to


Once you gain access, you can login to the frontend nodes, and These nodes are accessible via ssh from the GÖNET. If you come from the internet, first login to From there you can then reach the frontends. The frontends are meant for editing, compiling, and interacting with the batch system, but please don't use them for testing for more than a few minutes, since all users share resources on the frontends and will be impaired in their daily work, if you overuse them. gwdu101 is an AMD based system, while gwdu102 and gwdu103 are Intel based. If your software takes advantage of special CPU dependent features, it is recommended to use the same CPU architecture for compiling as targeted for running your jobs.

Hardware Overview

The following documentation is valid for this list of hardware:

Nodes # CPU GPU Cores Frequency Memory IB Queue Launched
gwdd[001-168] 168 Ivy-Bridge
Intel E5-2670 v2
none 2✕10 2.5 GHz 64 GB QDR mpi 2013-11
gwda[023-048] 25 Abu-Dhabi
AMD Opteron 6378
none 4✕16 2.4 GHz 256 GB QDR fat 2013-04
sa[001-032]* 32 Haswell
Intel E5-2680 v3
none 2✕12 2.5 GHz 256 GB QDR mpi 2015-03
72 Haswell
Intel E5-2640 v3
none 2✕8 2.6 GHz 128 GB QDR mpi 2015-03
gwde001 1 Haswell
Intel E7-4809 v3
none 4✕8 2.0 GHz 2 TB QDR fat+ 2016-01
dfa[001-015] 15 Broadwell
Intel E5-2650 v4
none 2✕12 2.2 GHz 512 GB FDR fat/fat+ 2016-08
dmp[011-076] 76 Broadwell
Intel E5-2650 v4
none 2✕12 2.2 GHz 128 GB FDR mpi 2016-08
dsu[001-005] 5 Haswell
Intel E5-4620 v3
none 4✕10 2.0 GHz 1.5 TB FDR fat+ 2016-08
gwdo[161-180]* 20 Ivy-Bridge
Intel E3-1270 v2
NVidia GTX 770 1✕4 3.5 GHz 16 GB none gpu 2014-01
dge[001-007] 7 Broadwell
Intel E5-2650 v4
NVidia GTX 1080 2✕12 2.2 GHz 128 GB FDR gpu 2016-08
dge[008-015] 8 Broadwell
Intel E5-2650 v4
NVidia GTX 980 2✕12 2.2 GHz 128 GB FDR gpu 2016-08
dge[016-045]* 30 Broadwell
Intel E5-2630 v4
NVidia GTX 1070 2✕10 2.2 GHz 64 GB none gpu 2017-06
dte[001-010] 10 Broadwell
Intel E5-2650 v4
NVidia K40 2✕12 2.2 GHz 128 GB FDR gpu 2016-08

Explanation: Systems marked with an asterisk (*) are only available for research group participating in the corresponding hosting agreement. GB = Gigabyte, TB = Terabyte, Gb/s = Gigabit per second, GHz = Gigahertz, GT/s = Giga transfer per second, IB = Infiniband, QDR = Quad data rate, FDR = Fourteen Data Rate.

For a complete overview of hardware, located in Göttingen, look at

Preparing Binaries

Most of the third-party software installed on the cluster is not located in the default path. To use it, the corresponding “module” must be loaded. Furthermore, through the module system you can setup environment settings for your compiler to use special libraries. The big advantage of this system is the (relative) simplicity with which one can coordinate environment settings, such as PATH, MANPATH, LD_LIBRARY_PATH and other relevant variables, dependent on the requirements of the use-case. You can find a list of installed modules, sorted by categories, by entering module avail on one of the frontends gwdu101 or gwdu102. The command module list gives you a list of currently loaded modules.

To use a module, you can explicitly load the version you want with module load software/version. If you leave out the version, the default version will be used. Logging off and back in will unload all modules, as well as module purge. You can unload single modules by entering module unload software.

The recommended compiler module for C, C++, and Fortran code is the default Intel compiler intel/compiler. We also provide GNU and Open64 compilers, the PGI compiler suite will follow. Open64 is often recommended for AMD CPUs, but we do not have experience with it. For math (BLAS and fftw3) the Intel MKL is a good default choice intel/mkl, with ACML being an alternative for AMD processors. Usually it is not necessary to use fftw3 modules alongside with the MKL, as the latter provides fftw support as well. Please note that the module python/scipy/mkl/0.12.0 provides Python's numpy and scipy libraries compiled with Intel MKL math integration, thus offering good math function performance in a scripting language.

intel/mpi and the various OpenMPI flavors are recommended for MPI, mostly due to the fact that the mvapich and mvapich2 libraries lack testing.

Running Jobs

Latest nodes

You can find all important information about the newest nodes here


User provided application documentation

Transfer Data

Environment Setup

Courses for High Performance Scientific Computing


This website uses cookies. By using the website, you agree with storing cookies on your computer. Also you acknowledge that you have read and understand our Privacy Policy. If you do not agree leave the website.More information about cookies