Library

Description: Large-eddy simulations (LES) with the new ICOsahedral Non-hydrostatic atmosphere model (ICON) covering Germany are evaluated for four days in spring 2013 using observational data from various sources. Reference simulations with the established Consortium for Small-scale Modelling (COSMO) numerical weather prediction model and further standard LES codes are performed and used as a reference. This comprehensive evaluation approach covers multiple parameters and scales, focusing on boundary-layer variables, clouds and precipitation. The evaluation points to the need to work on parametrizations influencing the surface energy balance, and possibly on ice cloud microphysics. The central purpose for the development and application of ICON in the LES configuration is the use of simulation results to improve the understanding of moist processes, as well as their parametrization in climate models. The evaluation thus aims at building confidence in the model's ability to simulate small- to mesoscale variability in turbulence, clouds and precipitation. The results are encouraging: the high-resolution model matches the observed variability much better at small- to mesoscales than the coarser resolved reference model. In its highest grid resolution, the simulated turbulence profiles are realistic and column water vapour matches the observed temporal variability at short time-scales. Despite being somewhat too large and too frequent, small cumulus clouds are well represented in comparison with satellite data, as is the shape of the cloud size spectrum. Variability of cloud water matches the satellite observations much better in ICON than in the reference model. In this sense, it is concluded that the model is fit for the purpose of using its output for parametrization development, despite the potential to improve further some important aspects of processes that are also parametrized in the high-resolution model.

Description: Due to the ubiquity of OpenMP and the rise of FPGA-based accelerators in the HPC world, several research groups have attempted to bring the two together by building OpenMP-to-FPGA compilers. This paper is a survey of the current state of the art (with a focus on the OpenMP target pragma). It first introduces and explains a design space for the compilers. Design space dimensions include how FPGA infrastructure is generated, how work is distributed, and where/how target outlining is done. A table concisely condenses the available information on the surveyed projects which are also summarized and compared. The paper concludes with possible future research directions.

Description: Field-programmable gate arrays (FPGAs) are of great interest for future high-performance computing and data analytics systems, since they are capable of efficient, highly-parallel data processing. Even though high-level synthesis became more popular in the last years, the effort of porting existing scientific software onto FPGAs is still considerable. We propose to use OpenMP target offloading as a solution, which we implement in a first prototype, making use of the preexisting OpenCL SDK of the FPGA vendor. Early results demonstrate the feasibility of this approach and also reveal that further optimizations will be necessary such that code can be written in an FPGA-agnostic way.

Description: Shape analysis provides principled means for understanding anatomical structures from medical images. The underlying notions of shape spaces, however, come with strict assumptions prohibiting the analysis of incomplete and/or topologically varying shapes. This work aims to alleviate these limitations by adapting the concept of soft correspondences. In particular, we present a graph-based learning approach for morphometric classification of disease states that is based on a generalized notion of shape correspondences in terms of functional maps. We demonstrate the performance of the derived classifier on the open-access ADNI database for differentiating normal controls and subjects with Alzheimer’s disease. Notably, our experiment shows that our approach can improve over state-of-the-art from geometric deep learning.