Universality of (2+1)-dimensional restricted solid-on-solid models

Extensive dynamical simulations of Restricted Solid on Solid models in $D=2+1$ dimensions have been done using parallel multisurface algorithms implemented on graphics cards. Numerical evidence is presented that these models exhibit KPZ surface growth scaling, irrespective of the step heights $N$. We show that by increasing $N$ the corrections to scaling increase, thus smaller step-sized models describe better the asymptotic, long-wave-scaling behavior

Bit-Vectorized GPU Implementation of a Stochastic Cellular Automaton Model for Surface Growth

Stochastic surface growth models aid in studying properties of universality classes like the Kardar--Paris--Zhang class. High precision results obtained from large scale computational studies can be transferred to many physical systems. Many properties, such as roughening and some two-time functions can be studied using stochastic cellular automaton (SCA) variants of stochastic models. Here we present a highly efficient SCA implementation of a surface growth model capable of simulating billions of lattice sites on a single GPU. We also provide insight into cases requiring arbitrary random probabilities which are not accessible through bit-vectorization.Comment: INES 2016, Budapest http://www.ines-conf.org/ines-conf/2016index.htm

Revisiting and modeling power-law distributions in empirical outage data of power systems

The size distribution of planned and forced outages and following restoration times in power systems have been studied for almost two decades and has drawn great interest as they display heavy tails. Understanding of this phenomenon has been done by various threshold models, which are self-tuned at their critical points, but as many papers pointed out, explanations are intuitive, and more empirical data is needed to support hypotheses. In this paper, the authors analyze outage data collected from various public sources to calculate the outage energy and outage duration exponents of possible power-law fits. Temporal thresholds are applied to identify crossovers from initial short-time behavior to power-law tails. We revisit and add to the possible explanations of the uniformness of these exponents. By performing power spectral analyses on the outage event time series and the outage duration time series, it is found that, on the one hand, while being overwhelmed by white noise, outage events show traits of self-organized criticality (SOC), which may be modeled by a crossover from random percolation to directed percolation branching process with dissipation, coupled to a conserved density. On the other hand, in responses to outages, the heavy tails in outage duration distributions could be a consequence of the highly optimized tolerance (HOT) mechanism, based on the optimized allocation of maintenance resources.Comment: 16 pages, 8 figure

Extremely large scale simulation of a Kardar-Parisi-Zhang model using graphics cards

The octahedron model introduced recently has been implemented onto graphics cards, which permits extremely large scale simulations via binary lattice gases and bit coded algorithms. We confirm scaling behaviour belonging to the 2d Kardar-Parisi-Zhang universality class and find a surface growth exponent: beta=0.2415(15) on 2^17 x 2^17 systems, ruling out beta=1/4 suggested by field theory. The maximum speed-up with respect to a single CPU is 240. The steady state has been analysed by finite size scaling and a growth exponent alpha=0.393(4) is found. Correction to scaling exponents are computed and the power-spectrum density of the steady state is determined. We calculate the universal scaling functions, cumulants and show that the limit distribution can be obtained by the sizes considered. We provide numerical fitting for the small and large tail behaviour of the steady state scaling function of the interface width.Comment: 7 pages, 8 figures, slightly modified, accepted version for PR

Dynamical heterogeneity and universality of power-grids

While weak, tuned asymmetry can improve, strong heterogeneity destroys synchronization in the electric power system. We study the level of heterogeneity, by comparing large high voltage (HV) power-grids of Europe and North America. We provide an analysis of power capacities and loads of various energy sources from the databases and found heavy tailed distributions with similar characteristics. Graph topological measures, community structures also exhibit strong similarities, while the cable admittance distributions can be well fitted with the same power-laws (PL), related to the length distributions. The community detection analysis shows the level of synchronization in different domains of the European HV power grids, by solving a set of swing equations. We provide numerical evidence for frustrated synchronization and Chimera states and point out the relation of topology and level of synchronization in the subsystems. We also provide empirical data analysis of the frequency heterogeneities within the Hungarian HV network and find q-Gaussian distributions related to super-statistics of time-lagged fluctuations, which agree well with former results on the Nordic Grid.Comment: 15 pages, 15 figure

Reconstructing Velocities of Migrating Birds from Weather Radar – A Case Study in Computational Sustainability

Bird migration occurs at the largest of global scales, but monitoring such movements can be challenging. In the US there is an operational network of weather radars providing freely accessible data for monitoring meteorological phenomena in the atmosphere. Individual radars are sensitive enough to detect birds, and can provide insight into migratory behaviors of birds at scales that are not possible using other sensors. Archived data from the WSR-88D network of US weather radars hold valuable and detailed information about the continent-scale migratory movements of birds over the last 20 years. However, significant technical challenges must be overcome to understand this information and harness its potential for science and conservation. We describe recent work on an AI system to quantify bird migration using radar data, which is part of the larger BirdCast project to model and forecast bird migration at large scales using radar, weather, and citizen science data

Research software on wings: Automating software publication with rich metadata

Publishing your research software in a publication repository is the first step on the path to making your software FAIR! But the publication of just the software itself is not quite enough: To truly enable findability, accessibility and reproducibility, as well as making your software correctly citable and unlock credit for your work, your software publication must come with the rich metadata that support these goals. But where will these metadata come from? And who should compile and publish them? Will RSEs have to become metadata experts as well now? In this talk, we argue that source code repositories and connected platforms often already provide many useful metadata, even if they are distributed over heterogeneous sources. We present an open source software toolchain that will help harvest these metadata, process and collate them, preparing them for submission to publication repositories. This toolchain can be automated via continuous integration platforms, and publish the prepared metadata with or without the respective software artifacts for open and closed source software alike. It can also feed the collated metadata back to source code repositories, or provide them in different formats for further reuse. The talk will outline the concept for the automated publication of research software with rich metadata and describe the current state of the software toolchain that is being developed. It will also detail the CI and publication platforms it will initially be available for, additional resources such as documentation and training materials, and give an outlook on sustainability and future development