Operational Numerical Weather Prediction systems based on Linux cluster architectures

The progress in weather forecast and atmospheric science has been always closely linked to the improvement of computing technology. In order to have more accurate weather forecasts and climate predictions, more powerful computing resources are needed, in addition to more complex and better-performing numerical models. To overcome such a large computing request, powerful workstations or massive parallel systems have been used. In the last few years, parallel architectures, based on the Linux operating system, have been introduced and became popular, representing real“high performance–low cost” systems. In this work the Linux cluster experience achieved at the Laboratory for Meteorology and Environmental Analysis (LaMMA-CNR-IBIMET) is described and tips and performances analysed

Operational mesoscale atmospheric dispersion prediction using a parallel computing cluster

An operational atmospheric dispersion prediction system is implemented on a cluster supercomputer for Online Emergency Response at the Kalpakkam nuclear site. This numerical system constitutes a parallel version of a nested grid meso-scale meteorological model MM5 coupled to a random walk particle dispersion model FLEXPART. The system provides 48-hour forecast of the local weather and radioactive plume dispersion due to hypothetical airborne releases in a range of 100 km around the site. The parallel code was implemented on different cluster configurations like distributed and shared memory systems. A 16-node dual Xeon distributed memory gigabit ethernet cluster has been found sufficient for operational applications. The runtime of a triple nested domain MM5 is about 4h for a 24h forecast. The system had been operated continuously for a few months and results were ported on the IMSc home page. Initial and periodic boundary condition data for MM5 are provided by NCMRWF, New Delhi. An alternative source is found to be NCEP, USA. These two sources provide the input data to the operational models at different spatial and temporal resolutions using different assimilation methods. A comparative study on the results of forecast is presented using these two data sources for present operational use. Improvement is noticed in rainfall forecasts that used NCEP data, probably because of its high spatial and temporal resolution

Implementation and scaling of the fully coupled Terrestrial Systems Modeling Platform (TerrSysMP) in a massively parallel supercomputing environment – a case study on JUQUEEN (IBM Blue Gene/Q)

Continental-scale hyper-resolution simulations constitute a grand challenge in characterizing non-linear feedbacks of states and fluxes of the coupled water, energy, and biogeochemical cycles of terrestrial systems. Tackling this challenge requires advanced coupling and supercomputing technologies for earth system models that are discussed in this study, utilizing the example of the implementation of the newly developed Terrestrial Systems Modeling Platform (TerrSysMP) on JUQUEEN (IBM Blue Gene/Q) of the Jülich Supercomputing Centre, Germany. The applied coupling strategies rely on the Multiple Program Multiple Data (MPMD) paradigm and require memory and load balancing considerations in the exchange of the coupling fields between different component models and allocation of computational resources, respectively. These considerations can be reached with advanced profiling and tracing tools leading to the efficient use of massively parallel computing environments, which is then mainly determined by the parallel performance of individual component models. However, the problem of model I/O and initialization in the peta-scale range requires major attention, because this constitutes a true big data challenge in the perspective of future exa-scale capabilities, which is unsolved

Enhancing Energy Production with Exascale HPC Methods

High Performance Computing (HPC) resources have become the key actor for achieving more ambitious challenges in many disciplines. In this step beyond, an explosion on the available parallelism and the use of special purpose processors are crucial. With such a goal, the HPC4E project applies new exascale HPC techniques to energy industry simulations, customizing them if necessary, and going beyond the state-of-the-art in the required HPC exascale simulations for different energy sources. In this paper, a general overview of these methods is presented as well as some specific preliminary results.The research leading to these results has received funding from the European Union's Horizon 2020 Programme (2014-2020) under the HPC4E Project (www.hpc4e.eu), grant agreement n° 689772, the Spanish Ministry of Economy and Competitiveness under the CODEC2 project (TIN2015-63562-R), and from the Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP). Computer time on Endeavour cluster is provided by the Intel Corporation, which enabled us to obtain the presented experimental results in uncertainty quantification in seismic imagingPostprint (author's final draft

Information-analytical system for cities of Perm region spatial development managment

To date, the practice of urban planning and design in Russia is based on the principles of the Soviet planned economy, while the current economic conditions require new approaches. Perm National Research Polytechnic University together with JSC «PROGNOZ», with the financial support of the Perm Region Government during the year of 2013 is developing an integrated spatial development IT-model, which allows the bringing together of socio-economic statistics, real estate state cadastre data, mathematical, statistical and adaptive methods library integrated with GIS under one platform. The main purpose of the system is the improvement of validity of decisions taken in different urban planning types, design improvements of Land Use and Development Regulations and prioritization of the municipal budget allocation. Decision support system is based on the Prognoz Platform (PP) - a next-generation BI platform for building high-tech business applications on a turnkey basis. PP allows to visualize and analyze operational data model and forecast processes. It has its own data warehouse designer which helps to build full-featured industrial BI systems based on the Prognoz Platform. PP Integration with the state information system for urban planning allows consideration of established local and federal law requirements for the quality of the urban environment, the prevailing land use and capabilities of municipal budgets. Model is to be used for the following tasks: Analysis of the effects of the changes in the administrative-territorial division of the region; Optimization of social facilities, taking into account standards of security, transport availability, etc. on the territory; Analysis of the current and future needs of transport infrastructure development, resource security of the region; Justification of the area choice for investment projects, etc. In the first phase the analogs of software that are used to solve problems of this kind, were identified. In the second phase of the model development typology and classification of simulation objects were studied, a list of necessary calculation and performance indicators/indicative indexes were determined, an algorithm providing integration with GIS and a prototype system were created. Prototype testing was conducted for the following tasks: Optimization of social facilities, taking into account federal regulations and budgetary constraints: selection of the site for the construction of a new school in the city; Justification of the energy infrastructure development scenario by combining mining settlements of Kizelovskoe and Gremyachinskoe municipal districts of Perm Region; Land Use Plan development for new residential area in New Lyady, Perm The next step of the analysis implies specifically housing and social infrastructure development challenges arising at the local government level, for the solution of which it is expedient to use the created model

Towards convection-resolving, global atmospheric simulations with the Model for Prediction Across Scales (MPAS) v3.1: an extreme scaling experiment

The Model for Prediction Across Scales (MPAS) is a novel set of Earth system simulation components and consists of an atmospheric model, an ocean model and a land-ice model. Its distinct features are the use of unstructured Voronoi meshes and C-grid discretisation to address shortcomings of global models on regular grids and the use of limited area models nested in a forcing data set, with respect to parallel scalability, numerical accuracy and physical consistency. This concept allows one to include the feedback of regional land use information on weather and climate at local and global scales in a consistent way, which is impossible to achieve with traditional limited area modelling approaches. Here, we present an in-depth evaluation of MPAS with regards to technical aspects of performing model runs and scalability for three medium-size meshes on four different high-performance computing (HPC) sites with different architectures and compilers.We uncover model limitations and identify new aspects for the model optimisation that are introduced by the use of unstructured Voronoi meshes.We further demonstrate the model performance of MPAS in terms of ist capability to reproduce the dynamics of the West African monsoon (WAM) and its associated precipitation in a pilot study. Constrained by available computational resources, we compare 11-month runs for two meshes with observations and a reference simulation from the Weather Research and Forecasting (WRF) model. We show that MPAS can reproduce the atmospheric dynamics on global and local scales in this experiment, but identify a precipitation excess for the West African region. Finally, we conduct extreme scaling tests on a global 3 km mesh with more than 65 million horizontal grid cells on up to half a million cores. We discuss necessary modifications of the model code to improve its parallel performance in general and specific to the HPC environment. We confirm good scaling (70% parallel efficiency or better) of the MPAS model and provide numbers on the computational requirements for experiments with the 3 km mesh. In doing so, we show that global, convection-resolving atmospheric simulations with MPAS are within reach of current and next generations of high-end computing facilities