Scientific Cluster Salzburg 1 (SCS1)

University of Salzburg

Salzburg | Website

Large equipment

Short Description

The Scientific Cluster Salzburg 1 consists of 3 identical machines boasting 32 cores and 1,5TB of RAM each. The cores are based on the processor Intel Xeon(R) Gold 6144 Prozessoren @ 3.50GHz (max. 4.20GHz) v4 which are mounted in 4 sockets per server. These machines are intended for scalable applications who address all cores per system via the 10Gbit interconnect.
The operating system in use is Red-Hat Linux.
(For further Informations: https://hpc.sbg.ac.at)

Contact Person

Prof. DI Dr. Andreas Schröder

Research Services

Provision of high performance computing

Methods & Expertise for Research Infrastructure

Mathematics
Complex simulations and numerical applications, as well as calculations of linear models (multivariate statistics) and sample size planning. In particular, simulations of natural scientific and technical processes with finite element methods (andreas.schroeder@sbg.ac.at).

Informatics
Use of methods from algorithmic theory and of randomized algorithms (especially for the analysis of large networks). These techniques are combined with methods of discrete mathematics (robert.elsaesser@sbg.ac.at).

Biology
Computations in the field of community ecology: e.g. simulation of the influence of assembly-rules and intra-/interspecific competition to floral and vegetal traits of plant communities. Computations of relations of flower colours and bloom visitors with non-linear least square regressions.

Psychology
Used methods: FreeSurfer with Singularity, Dynamic Causal Modelling (DCM), ERF and time-frequency analysis
Expertise in: Longitudinal Structural MRI analysis, EEG / MEG analysis, Digital signal processing"

Prof. DI Dr. Andreas Schröder
Fachbereich Mathematik
0043 662 8044 5316
andreas.schroeder@sbg.ac.at
https://hpc.sbg.ac.at/
Please contact us via science.plus@sbg.ac.at, or contact the responsible person for this section, mentioned in the contact field
A fictitious domain method for the simulation of thermoelastic deformations in NC‐milling processes.
2018
Andreas Byfut, Andreas Schröder
Int J Numer Meth Engng.
https://onlinelibrary.wiley.com/doi/abs/10.1002/nme.5609

Marching volume polytopes algorithm
2019
Andreas Byfut, Friederike Hellwig, Andreas Schröder
Int J Numer Meth Engng.
https://onlinelibrary.wiley.com/doi/10.1002/nme.5995

Small-sample performance and underlying assumptions of a bootstrap-based inference method for a general analysis of covariance model with possibly heteroskedastic and nonnormal Errors
2019
Zimmermann G, Pauly M, and Bathke AC
Stat Methods Med Res, accepted, doi: 10.1177/0962280218817796

Sample size calculation and blinded recalculation for analysis of covariance models with multiple random covariates
2018
Zimmermann G, Kieser M, and Bathke AC
Journal of Biopharmaceutical Statistics, arXiv:1806.03673v1 [stat.ME]