Dynamic Task-Scheduling Based Parallel Processing on Watershed Distributed Eco-Hydrological Model

Abstract:

Watershed distributed eco-hydrological model is an important tool in the field of global change research. Due to the complexity of eco-hydrological model, watershed distributed eco-hydrological simulation requires large amounts of computations. The compution overhead turns to be a big prolem for those basin areas. Another challenge is that the traditional sequential computation techniques cannot meet the requirements of watershed eco-hydrological model, which highly limits the application of watershed distributed eco-hydrological model in large scale areas. This paper proposed a dynamic task-scheduling based parallel processing method for eco-hydrological model. The whole simulation task are firstly decoupled into independent grid based parallel processing tasks based on the relation of upstream and downstream sequence. Then a dynamic task-tree was built up according to the dependency of each cell in the watershed, which can generate dynamic task scheduling sequence. Following the task scheduling sequence, PBS task scheduler submitted workloads, realizing parallel calculation. This approach was applied in the watershed of Walnut Gulch watershed in Arizona, USA. The result showed that this method can highly improves the efficiency of watershed eco-hydrological modeling almost 6 times compared to that of the traditional sequential eco-hydrological modeling. Therefore, this approach can effectively promote the applications of watershed eco-hydrological model.

• Periodical: Applied Mechanics and Materials (Volumes 556-562)
• Pages: 3492-3495
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.556-562.3492
• Online since: May 2014
• Authors: Yao Li*, Da Lin Jiang
• Keywords: Dynamic Task-Scheduling, Parallel Processing, Watershed Eco-Hydrological Model

References

[1] Chen L J, Zhu A X, Qin C Z, et al. 2011. Review of eco-hydrological models of watershed scale. Progress in Geography, 30(5): 535-544.

[2] Band L. E, Patterson P, Nemani R, et al., 1993, Forest ecosystem processes at the watershed scale: Incorporating hillslope hydrology. Agricultural and Forest Meteorology, 63: 93-126.

DOI: 10.1016/0168-1923(93)90024-c

[3] Waring R H, Running S W, 1998. Forest ecosystems: analysis at multiple scales, 2nd Edition, San Diego: Academic Press.

[4] Arora V., 2002. Modeling vegetation as a dynamic component in Soil-vegetation- atmosphere transfer schemes and hydrological models. Reviews of Geophysics, 40(2): 1-26.

DOI: 10.1029/2001rg000103

[5] Wigmosta M S, Vail L W, Lettenmaier D P. A Distributed hydrology-vegetation model for complex terrain. Water Resources Research, 1994, 30(6): 1665-1679.

DOI: 10.1029/94wr00436

[6] Band L E, Tague C L, Groffman P, et al., 2001. Forest ecosystem processes at the watershed scale: Hydrological and ecological controls of nitrogen export. Hydrological Processes, 15, 2013-(2028).

DOI: 10.1002/hyp.253

[7] Tague C L, Band L E, 2004. RHESSys: regional hydro-ecologic simulation system: an object- oriented approach to spatially distributed modeling of carbon, water and nutrient cycling. Earth Interactions, 8(19): 1-42.

DOI: 10.1175/1087-3562(2004)8<1:rrhsso>2.0.co;2

[8] Cheng, J.R.C., Hunter, R.M., Cheng, H. -P., et al. 2005. Parallelization of the WASH123D code phase II: Coupled two-dimensional overland and three-dimensional subsurface flows. in: Proceedings of World Water and Environmental Resources Congress 2005, Anchorage, Alaska. United States, May 15-19, 2005, doi: 10.

DOI: 10.1061/40792(173)279

[9] Neal, J., Fewtrell, T., Trigg, M., 2009. Parallelisation of storage cell flood models using OpenMPI. Environ. Modell. Softw. 24, 872–877 Schmuck F, Haskin R. 2002. GPFS: A shared-disk file system for large computing clusters. Proceedings of the First USENIX Conference on File and Storage Technologies, Berkeley, USA: 28-30 January.

DOI: 10.1016/j.envsoft.2008.12.004

[10] Sanders, B.F., Schubert, J.E., Detwiler, R.L., 2010. ParBreZo: a parallel, unstructured grid, Godunov-type, shallow-water code for high resolution flood inundation modeling at the regional scale. Adv. Water Resour. 33, 1456–1467.

DOI: 10.1016/j.advwatres.2010.07.007

[11] Wu, Y. -S., Zhang, K., Ding, C., Pruess, K., Elmroth, E., Bodvarsson, G.S., 2002. An efficient parallel-computing method for modeling nonisothermal multiphase flow and multicomponent transport in porous and fractured media. Adv. WaterResour. 25, 243–261.

DOI: 10.1016/s0309-1708(02)00006-4

[12] Hammond, G.E., Lichtner, P.C., 2010. Field-scale model for the natural attenuation of uranium at the Hanford 300 Area using high-performance computing. Water Resour. Res. 46, W09527. doi: 10. 1029/ 2009WR008819.

DOI: 10.1029/2009wr008819