Multicomponent application paradigms have gained prominence in many significant multidisciplinary scientific applications. In this work, we propose a software strategy called dynamic component extension for multicomponent applications to improve application performance by minimizing processor idling. In this strategy, the processor space of a component is dynamically extended to include the processors of other components during certain computationally intensive phases of the component. We demonstrate its use in improving the performance of one of the most prominent multicomponent applications, the community climate system model (CCSM). In this application, we dynamically extend the atmosphere component to minimize the idling in other components caused by large periodic temporal load imbalances in the atmosphere component. By means of experiments on different parallel platforms with different numbers of processors, we show that using our strategy can lead to about 15% reduction and savings of several days in execution times of CCSM for 1000-year simulation runs.
Carr, G. (2005). An introduction to load balancing CCSM3 components . In Proceedings of Software Engineering Working Group (SEWG) Meeting, CCSM Workshop, NCAR.
2.
Carr, G., Cordery, M., Drake, J., Ham, M., Hoffman, F., and Worley, P. (2005). Porting and performance of the community climate system model (CCSM3) on the Cray X1. In Proceedings of the 2005 Cray Users Group (CUG) Meeting, Albuquerque, New Mexico.
3.
Chow, P. and Addison, C. (1999). An overview on current multiphysics software strategies for coupled applications with interacting physics on parallel and distributed computers. In Proceedings of the 11th International Conference on Domain Decomposition Methods in Sciences & Engineering.
4.
Collins, W., Bitz, C., Blackmon, M., Bonan, G., Bretherton, C., Carton, J., Chang, P., Doney, S., Hack, J., Henderson, T., Kiehl, J., Large, W., McKenna, D., Santer, B., and Smith, R. (1998). The community climate system model: CCSM3.
5.
Collins, N., Theurich, G., DeLuca, C., Suarez, M., Trayanov, A., Balaji, V., Li., P., Yang, W., Hill, C., and da Silva, A. (2005). Design and implementation of components in the Earth system modeling framework, International Journal of High Performance Computing Applications, 19(3): 341-350.
6.
Coveney, P., Fabritiis, G.D., Harvey, M., Pickles, S., and Porter, A. (2005). On steering coupled models. In e-Science All Hands Meeting.
7.
Delgado-Buscalioni, R., Coveney, P., Riley, G., and Ford, R. (2005). Hybrid molecular-continuum fluid models: Implementation within a general coupling framework, Philos. Trans. Roy. Soc. London Ser. A, 363.
8.
Dorneles, R., Rizzi, R., Diverio, T., and Navaux., P. (2003). Dynamic load balancing in PC clusters: An application to a multiphysics model. In Proceedings of 15th Symposium on Computer Architecture and High Performance Computing, pp. 192-198.
9.
Drake, J., Foster, I., Michalakes, J., Toonen, B., and Worley, P. (1995). Design and performance of a scalable parallel community climate model, Parallel Computing, 21(10): 1571-1592.
10.
Ford, R. and Burton, P. (1998). Load balancing physics routines. In Proceedings of the 8th ECMWF Workshop on the Use of Parallel Processors in Meteorology.
11.
Foster, I. and Toonen, B. (1994). Load balancing algorithms for climate models. In Proceedings of the Scalable High Performance Computing Conference, pp. 674-681.
12.
Larson, J. (2006). Some organising principles for coupling in multiphysics and multiscale models. In Computational Techniques and Applications Conference (CTAC'06).
13.
Larson, J., Jacob, R., and Ong, E. (2005). The Model Coupling Toolkit: A new Fortran90 toolkit for building multiphysics parallel coupled models, International Journal of High Performance Computing Applications, 19: 277-292.
14.
Lawrence, C. (1997). An overview of three approaches to multidisciplinary aeropropulsion simulations. Technical Report NASA TM-107443, NASA.
15.
Lefantzi, S. and Ray, J. (2003). A component-based scientific toolkit for reacting flows. In Proceedings of the 2nd MIT Conference on Computational Fluid and Solid Mechanics, pp. 1401-1405.
16.
Lermusiaux, P., Evangelinos, C., Tian, R., Haley, P., McCarthy, J., Patrikalakis, N., Robinson, A., and Schmidt, H. (2004). Adaptive coupled physical and biogeochemical ocean predictions: A conceptual basis. In Computational Science, International Conference on Computational Science (ICCS) 2004, edited by F. Darema.
17.
Michalakes, J. (1991). Analysis of workload and load balancing issues in the NCAR community climate model. Technical Report ANL/MCS-TM-144, Argonne National Laboratory, Argonne, Illinois.
18.
Michalakes, J. and Nanjundiah, R. (1994). Computational load in model physics of the parallel NCAR community climate model. Technical Report ANL/MCS-TM-186, Argonne National Laboratory, Argonne, Illinois.
19.
Michalakes, J., Canfield, T., Nanjundiah, R., Hammond, S., and Grell, G. (1994). Parallel implementation, validation, and performance of MM5. In Parallel Supercomputing in Atmospheric Science. River Edge, NJ: World Scientific.
20.
Michalakes, J., Dudhia, J., Gill, D., Henderson, T., Klemp, J., Skamarock, W., and Wang, W. (2004). The weather research and forecast model: Software architecture and performance. In The Eleventh ECMWF Workshop on the Use of High Performance Computing in Meteorology.
21.
Mirin, A. and Sawyer, W. (2005). A scalable implementation of a finite-volume dynamical core in the community atmosphere model, International Journal of High Performance Computing Applications, 19(3).
22.
Mirin, A. and Worley, P. (2007). Extending scalability of the community atmosphere model. In Journal of Physics: Conference Series, 78 (Proceedings of SciDAC 2007), Boston, MA.
23.
Muszala, S., Alaghband, G., Connors, D., and Hack, J. (2004). A VFSA scheduler for radiative transfer data in climate models. In 17th International Conference on Parallel and Distributed Computing Systems.
24.
Muszala, S., Connors, D., Hack, J., and Alaghband, G. (2006). The promise of load balancing the parameterization of moist convection using a model data load index, Journal of Atmospheric and Oceanic Technology, 23(525b).
25.
Nanjundiah, R. (1998). Strategies for parallel implementation of a global spectral atmospheric general circulation model. In 5th International Conference on High Performance Computing.
26.
Nanjundiah, R. (2006). Seasonal simulation of the monsoon with the NCMRWF model, Current Science, 78: 869-875.
27.
Pauluis, O. and Emanuel, K. (2004). Numerical instability resulting from infrequent calculation of radiative heating, Monthly Weather Review, 132: 673-686.
28.
Toth, G., Volberg, O., Ridley, A., Gombosi, T., DeZeeuw, D., Hanson, K., Chesney, D., Stout, Q., Powell, K., Kane, K., and Oehmke, R. (2003). A physics-based software framework for Sun-Earth connection modeling. In Multiscale Coupling of Sun-Earth Processes, Proceedings of the Conference on the Sun-Earth Connection , pp. 383-397.
