Abstract
The speed of execution of resource intensive application depends mostly on the performance of the underlying hardware and network infrastructure. The overall performance of complex Grid applications that include different types of processing in the same Grid job is difficult to predict reliably. In this paper we define several key performance indicators and collect data from the execution of a resource intensive environmental modeling application on the regional resources of the European Grid Infrastructure. The application is based on the Models-3 system, consisting of three components: meteorological pre-processor MM5, chemical transport model CMAQ and emission pre-processor SMOKE. The computations are resource intensive with respect to the input and output data which stress both the computational and data capabilities of the resource centers. In the paper we analyze the relative importance of these indicators and draw conclusions, regarding the optimal use of available resource centers.
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References
Ganev, K., Syrakov, D., Prodanova, M., Hristov, H., Atanasov, E., Gurov, T., Karaivanova, A., Miloshev, N.: Grid computing for air quality and environmental studies in Bulgaria. In: Proceedings of 23rd EnviroInfo 2009 Conference, Berlin, 9–11 Sept 2009, pp. 147–155 (2009)
Ganev, K., Syrakov, D., Gadzhev, G., Prodanova, M., Jordanov, G., Miloshev, N., Todorova, A.: Joint analysis of regional scale transport and transformation of air pollution from road and ship transport. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds.) LSSC 2009. LNCS, vol. 5910, pp. 180–187. Springer, Heidelberg (2010)
Grell, G.A., Dudhia, J., Stauffer, D.R.: A description of the Fifth Generation Penn State/NCAR Mesoscale Model (MM5), NCARTechnical Note, NCAR TN-398-STR, 138 pp. (1994)
Byun, D., Ching, J.: Science Algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System, EPA Report 600/R-99/030, Washington, DC (1999)
CEP, Sparse Matrix Operator Kernel Emission (SMOKE) Modeling System, University of Carolina, Carolina Environmental Programs, Research Triangle Park, North Carolina (2003)
Gadzhev, G.K., Ganev, K.G., Miloshev, N.G., Syrakov, D.E., Prodanova, M.: Numerical study of the atmospheric composition in Bulgaria. Comput. Math. Appl. 65(3), 402–422 (2013)
Atanassov, E., Gurov, T., Karaivanova, A.: Capabilities of the HPC cluster at IICT-BAS. J. Automatika and Informatika 2, 7–11 (2011). ISSN: 0861–7562 (in Bulgarian)
Hristova, R.: Monitoring of business processes in the EGI. In: Conference Proceedings of the 6th International Conference ISGT, 01–03 June 2012, Sofia, Bulgaria, pp. 294–300 (2012)
MPIEXEC Tutorial. https://www.osc.edu/djohnson/mpiexec/
Acknowledgments
The research work reported in the paper is partly supported by the project AComIn - Advanced Computing for Innovation, grant 316087, funded by the FP7 Capacity Programme (Research Potential of Convergence Regions), and by the National Science Fund of Bulgaria under Grants DCVP02/1 (SuperCA++).
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Hristova, R., Ivanovska, S., Durchova, M. (2014). Performance Analysis of the Regional Grid Resources for an Environmental Modeling Application. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2013. Lecture Notes in Computer Science(), vol 8353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43880-0_58
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DOI: https://doi.org/10.1007/978-3-662-43880-0_58
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