arXiv subjects

Ferdinand Jamitzky

Publications and source records attributed to Ferdinand Jamitzky.

4 recordsLinked to original sources

arXiv2016

Extreme Scale-out SuperMUC Phase 2 - lessons learned

In spring 2015, the Leibniz Supercomputing Centre (Leibniz-Rechenzentrum, LRZ), installed their new Peta-Scale System SuperMUC Phase2. Selected users were invited for a 28 day extreme scale-out block operation during which they were allowed to use the full system for their applications. The following projects participated in the extreme scale-out workshop: BQCD (Quantum Physics), SeisSol (Geophysics, Seismics), GPI-2/GASPI (Toolkit for HPC), Seven-League Hydro (Astrophysics), ILBDC (Lattice Boltzmann CFD), Iphigenie (Molecular Dynamic), FLASH (Astrophysics), GADGET (Cosmological Dynamics), PSC (Plasma Physics), waLBerla (Lattice Boltzmann CFD), Musubi (Lattice Boltzmann CFD), Vertex3D (Stellar Astrophysics), CIAO (Combustion CFD), and LS1-Mardyn (Material Science). The projects were allowed to use the machine exclusively during the 28 day period, which corresponds to a total of 63.4 million core-hours, of which 43.8 million core-hours were used by the applications, resulting in a utilization of 69%. The top 3 users were using 15.2, 6.4, and 4.7 million core-hours, respectively.

cs.DC↗

arXiv2008

Characterizing Synchronization in Time Series using Information Measures Extracted from Symbolic Representations

We present a methodology to characterize synchronization in time series based on symbolic representations. A symbol is linked to a sequence of numbers through the rank-order of its values. A representation of a time series results after mapping all sequences into symbols. We propose a transcription scheme between symbolic representations to study the dynamics of coupled systems. This scheme allows us to use elements of group theory and to derive information measures to assess the degree of synchronization. We apply our method to a prototype non-linear system which displays a rich coupled dynamics.

physics.data-an↗

arXiv2004

Detecting non-linearities in data sets. Characterization of Fourier phase maps using the Weighted Scaling Indices

We present a methodology for detecting non-linearities in data sets based on the characterization of the structural features of the Fourier phase maps. A Fourier phase map is a 2D set of points $M= \{(ϕ_{\vec{k}}, ϕ_{\vec{k} + \vecΔ})\}$, where $ ϕ_{\vec{k}}$ is the phase of the $k$-mode of the Fourier transform of the data set and $\vecΔ$ a phase shift. The information thus rendered on this space is analyzed using the spectrum of weighted scaling indices to detect phase coupling at any scale $\vecΔ$. We propose a statistical test of significance based on the comparison of the properties of phase maps created from both the original data and surrogate realizations. We have applied our method to the Lorenz system and the logarithmic stock returns of the Dow Jones index. Applications to higher dimensional data are straightforward. The results indicate that both the Lorenz system and the Dow Jones time series exhibit significant signatures of non-linear behavior.

physics.data-an↗

arXiv1999

Reconnection rate for the steady-state Petschek model

Reconnection rate is found for the canonical simplest case of steady-state two-dimensional symmetric reconnection in an incompressible plasma by matching of outer Petschek solution and internal diffusion region solution. The reconnection rate obtained naturally incorporates both Sweet-Parker and Petschek regimes, the latter seems to be possible only for the case with strongly localized resistivity.

physics.plasm-ph↗