Table of Contents
High Performance Computing
This is the old HPC documentation. It is being migrated to another site.
You can find a more up-to-date version here.
Welcome to the official documentation of the Scientific Compute Cluster (SCC). It is the high performance computing system operated by the GWDG for both the Max Planck Society and the University of Göttingen.
This documentation will give you the necessary information to get access to the system, find the right software or compile your own, and run calculations.
Software
We provide a growing number of programs, libraries, and software on our system. These are available as modules. You can find a list with the module avail command and load them via module load. For example, if you want to run GROMACS, you simply use module load gromacs to get the most recent version. Additionally, we use a package management tool called Spack to install software. A guide on how to use modules and Spack is available here.
We provide different compilers and libraries if you want to compile your software on your own. As with the rest of the software, these are available as modules. These include gcc, intel, and nvhpc as compilers, openmpi, intel-oneapi-mpi as MPI libraries, and others such as mpi4py, fftw and hdf5. You can find more specific instructions on code compilation on our dedicated page.
Performance Engineering and Analysis
Performance engineering, analyis and optimization is imperative for HPC applications especially considering the huge amount of resources spent on assembling and operating large computing systems with complex microprocessors (X-PUs) and memory architectures.
Performance analysis and optimization of HPC applications involve mainly three steps, application instrumentation, run-time measurements of key events and visual analysis of profiles and events traces.
Performance tools currently available for use in our clusters, for CPUs and GPUs are: “LIKWID”, “Score-P”, “Vampir”, and “Scalasca” for CPUs and Nsight Toolset (System, Compute and Graphic) for Nvidia GPUs. The tools and how to use them in the cluster are documented in Performance Tools page.