A communication library for the parallelization of air quality models on structured grids (original) (raw)
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Parallel and GRID Implementation of a Large Scale Air Pollution Model
Lecture Notes in Computer Science, 2007
Large-scale environmental models are powerful tools, designed to meet the increasing demand in various environmental studies. The atmosphere is the most dynamic component of the environment, where the pollutants and other chemical species actively interact with each other, and can quickly be moved in a very long distance. Therefore the advanced modeling is usually done in a large computational domain. Moreover, all relevant physical, chemical and photochemical processes should be taken into account, which heavily depend on the meteorological conditions. All this makes the air pollution modeling a huge and rather difficult computational task, requiring a large amount of computational power. The most powerful supercomputers have been used for the development and test runs of such a model, the Danish Eulerin Model (DEM). Distributed parallel computing via MPI is one of the most efficient techniques in achieving good performance and getting results in real time. The quickly advancing GRID computing technology is another powerful tool that can be used to reach higher level of performance of such a huge model. Both techniques and their inherent problems are discussed in this paper. Results of numerical experiments are presented and analysed and some conclusions are drown, based on the experiments.
A Hybrid Parallelization of Air Quality Model with MPI and OpenMP
Lecture Notes in Computer Science, 2012
This paper presents the parallelization of FARM, a 3D Eulerian chemical-transport model on structured and nested grids. The parallelization has been developed using the MPI library and OpenMP directives implementing a Master-Worker strategy. Benchmarking in different architectures is also discussed.
Flexible Two-Level Parallel Implementations of a Large Air Pollution Model
Large scale air pollution models are powerful tools, designed to meet the increasing demand in different environmental studies. The atmosphere is the most dynamic component of the environment, where the pollutants can quickly be moved in a very long distance. Therefore the advanced modeling must be done in a large computational domain. Moreover, all relevant physical, chemical and photochemical processes must be taken into account. The speed of these processes vary in a wide range. This fact implies that a small time step must be used in order to achieve both numerical stability and sufficient accuracy of the results. Thus the numerical treatment of such an air pollution model becomes in many cases a huge computational problem, a challenging task for the most powerful up-to-date supercomputers. The Danish Eulerian Model (DEM) is used in this work. The paper fo-cuses on the efficient parallel implementation of DEM on powerful parallel supercomputers. We present a variety of performance and scalability results, obtained on different parallel machines by using standard paral-lelization tools (MPI for distributed-memory parallelism and OpenMP for shared-memory parallelism). It is shown by experiments that MPI and OpenMP can both be used on separate levels of parallelism to get the best use of the clustered parallel machines. Application of the results in the environmental studies, related to high ozone concentrations in the air, is illustrated by some plots in the last section.
Parallel Implementation of a Large-Scale 3-D Air Pollution Model
Lecture Notes in Computer Science, 2001
Air pollution models can efficiently be used in different environmental studies. The atmosphere is the most dynamic component of the environment, where the pollutants can be transported over very long distances. Therefore the models must be defined on a large space domain. Moreover, all relevant physical and chemical processes must be adequately described. This leads to huge computational tasks. That is why it is difficult to handle numerically such models even on the most powerful up-to-date supercomputers. The particular model used in this study is the Danish Eulerian Model. The numerical methods used in the advection-diffusion part of this model consist of finite elements (for discretizing the spatial derivatives) followed by predictor-corrector schemes with several different correctors (in the numerical treatment of the resulting systems of ordinary differential equations). Implicit methods for the solution of stiff systems of ordinary differential equations are used in the chemistry part. This implies the use of Newton-like iterative methods. A special sparse matrix technique is applied in order to increase the efficiency. The model is constantly updated with new faster and more accurate numerical methods. The three-dimensional version of the Danish Eulerian Model is presented in this work. The model is defined on a space domain of 4800 km × 4800 km that covers the whole of Europe together with parts of Asia, Africa and the Atlantic Ocean. A chemical scheme with 35 species is used in this version. Two parallel implementations are discussed; the first one for shared memory parallel computers, the second one -the newly developed version for distributed memory computers. Standard tools are used to achieve parallelism: OpenMP for shared memory computers and MPI for distributed memory computers. Results from many experiments, which were carried out on a SUN SMP cluster and on a CRAY T3E at the Edinburgh Parallel Computer Centre (EPCC), are presented and analyzed.
Performance results of a large-scale air pollution model on two parallel computers
International Journal of Environment and Pollution, 2004
The size of the computational task obtained after the appropriate splitting and discretization procedures of the system of partial differential equations in large air pollution models is enormous. Even when computers with several GFLOPS top performance are in use, it is difficult to solve this problem efficiently and, furthermore, to prepare codes that can be used for operating purposes. Some performance-comparison results obtained by runs of the parallelized via MPI unified variable-grid version of the Danish Eulerian model for long-range transport of air pollutants on two parallel computer architectures are presented. The test numerical experiments are performed on a Beowulf-like cluster setup of four two-processor Macintosh G4 machines and a multiprocessor SUN (up to 32 processors). The Message Passing Interface (MPI) standard is used on both distributed and shared memory parallel computer platforms. Timing results, speed-up and parallel efficiency of the main computational stages as well as of the whole model are presented.
Runtime support for collaborative air pollution models
2002
We present the design of a computational methodology and a prototype software environment and infrastructure that promotes collaboration in air pollution simulations in a highly interactive way during the development and use of the simulation engine over scalable distributed systems of heterogeneous computational components. In particular we focus on the software runtime system to support such a distributed simulation engine.
Parallel runs of a large air pollution model on a grid of Sun computers
Mathematics and Computers in Simulation, 2004
Large-scale air pollution models can successfully be used in different environmental studies. These models are described mathematically by systems of partial differential equations. Splitting procedures followed by discretization of the spatial derivatives lead to several large systems of ordinary differential equations of order up to 80 millions. These systems have to be handled numerically at up to 250,000 time-steps. Furthermore, many scenarios are often to be run in order to study the dependence of the model results on the variation of some key parameters (as, for example, the emissions). Such huge computational tasks can successfully be treated only if: (i) fast and sufficiently accurate numerical methods are used and (ii) the models can efficiently be run on parallel computers.
Parallel Computations with Large-scale Air Pollution Models
2003
Large-scale mathematical models are very powerful tools in the efforts to provide more information and more detailed information about the pollution levels, especially about pollution levels which exceed certain critical values.. However, the model used must satisfy at least two conditions: (i) it must be verified that the model results are reliable and (ii) it should be possible to carry out different study by using the model. It is clear that comprehensive studies about relationships between different input parameters and the model results can only be carried out (a) if the numerical methods used in the model are sufficiently fast and (b) if the code runs efficiently on the available high-speed computers. Some results obtained recently by a new unified version of the Danish Eulerian Model will be presented in this paper.
Atmospheric Pollution Research, 2010
The Community Multiscale Air Quality Model (CMAQ) is a comprehensive three-dimensional "one-atmosphere" air quality model that is now routinely used to address urban, regional-scale and continental-scale multipollutant issues such as ozone, particulate matter, and air toxics. Several updates have been made to CMAQ by the scientific community to enhance its capabilities and to provide alternative science treatments of some of the relevant governing processes. The Advanced Modeling System for Transport, Emissions, Reactions and Deposition of Atmospheric Matter (AMSTERDAM) is one such adaptation of CMAQ that adds an Advanced Plume-in-grid Treatment (APT) for resolving sub-grid scale processes associated with emissions from elevated point sources. It also incorporates a state-of-the-science alternative treatment for aerosol processes based on the Model of Aerosol Dynamics, Reaction, Ionization and Dissolution (MADRID). AMSTERDAM is configured to provide flexibility to the model user in selecting options for the new science modules. This paper describes the parallelization of AMSTERDAM to make it a practical tool for plume-in-grid (PinG) treatment of a large number of point sources, and presents results from its application to the central and eastern United States for summer and winter periods in 2002. Over 150 coal-fired power plants in the domain with high emissions of sulfur dioxide (SO 2) and nitrogen oxides (NO X) were selected for PinG treatment in the CMAQ-MADRID-APT configuration of AMSTERDAM used for this application. Although both model configurations (grid-only and PinG) give similar model performance results (an aggregate measure of model skill), the results show significant differences between the two versions in the specific nature of the predicted spatial distribution of ozone and PM 2.5 concentrations. These differences can be important in determining source contributions to ambient concentrations. A companion paper examines the differences in the predicted contributions of hypothetical source regions from the two configurations of the model.
International Conference on Information Technology: Coding Computing, ITCC, 2004
Leveraging Grid Computing technology, i.e. the virtualization of distributed computing and data resources such as processing, network bandwidth and storage capacity to create a single system image, we present a Grid Air Quality Forecast System (G-AQFS). The Modeling system consists of meteorological and dispersion models coupled in cascade. The computational workflow of the Modeling system is defined by means of DAGs (Direct Acyclic Graph). A simple system is presented to manage and schedule the computational Grid resources. In particular, the algorithm developed for the Work Flow Scheduler named Depth-First Search Job with Priority (DFSP) is illustrated. As case study the system has been applied over Salento area, in the Apulia region (South-eastern Italy), to simulate ground level ozone concentration. Model predictions have been compared with field measurements, with reasonable results.