Parallelization of a distributed ecohydrological model
WaSSI-C is an ecohydrological model which couples water and carbon cycles with water use efficiency (WUE) derived from global eddy flux observations. However, a significant limitation of the WaSSI-C model is that it only runs serially. High resolution simulations at a large scale are therefore compu...
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| Veröffentlicht in: | Environmental modelling & software : with environment data news Jg. 101; S. 51 - 63 |
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| Abstract | WaSSI-C is an ecohydrological model which couples water and carbon cycles with water use efficiency (WUE) derived from global eddy flux observations. However, a significant limitation of the WaSSI-C model is that it only runs serially. High resolution simulations at a large scale are therefore computationally expensive and cause a run-time memory burden. Using distributed (MPI) and shared (OpenMP) memory parallelism techniques, we revised the original model as dWaSSI-C. We showed that using MPI was effective in reducing the computational run-time and memory use. Two experiments were carried out to simulate water and carbon fluxes over the Australian continent to test the sensitivity of the parallelized model to input data-sets of different spatial resolutions, as well as to WUE parameters for different vegetation types. These simulations were completed within minutes using dWaSSI-C, whereas they would not have been possible with the serial version. The dWaSSI-C model was able to simulate the seasonal dynamics of gross ecosystem productivity (GEP) reasonably well when compared to observations at four eddy flux sites. Sensitivity analysis showed that simulated GEP was more sensitive to WUE during the summer compared to winter in Australia, and woody savannas and grasslands showed higher sensitivity than evergreen broadleaf forests and shrublands. Although our results are model-specific, the parallelization approach can be adopted in other similar ecosystem models for large scale applications.
•The WaSSI-C ecohydrological model serially calculates water and carbon fluxes and is therefore restricted to coarse resolution simulations.•MPI and OpenMP techniques significantly reduced computing time allowing for simulations at much higher spatial resolutions.•This approach was demonstrated at a 5 km resolution over the Australian continent, and could be performed in minutes.•The same parallelization approach can be applied to other complex ecohydrological models. |
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| AbstractList | WaSSI-C is an ecohydrological model which couples water and carbon cycles with water use efficiency (WUE) derived from global eddy flux observations. However, a significant limitation of the WaSSI-C model is that it only runs serially. High resolution simulations at a large scale are therefore computationally expensive and cause a run-time memory burden. Using distributed (MPI) and shared (OpenMP) memory parallelism techniques, we revised the original model as dWaSSI-C. We showed that using MPI was effective in reducing the computational run-time and memory use. Two experiments were carried out to simulate water and carbon fluxes over the Australian continent to test the sensitivity of the parallelized model to input data-sets of different spatial resolutions, as well as to WUE parameters for different vegetation types. These simulations were completed within minutes using dWaSSI-C, whereas they would not have been possible with the serial version. The dWaSSI-C model was able to simulate the seasonal dynamics of gross ecosystem productivity (GEP) reasonably well when compared to observations at four eddy flux sites. Sensitivity analysis showed that simulated GEP was more sensitive to WUE during the summer compared to winter in Australia, and woody savannas and grasslands showed higher sensitivity than evergreen broadleaf forests and shrublands. Although our results are model-specific, the parallelization approach can be adopted in other similar ecosystem models for large scale applications. WaSSI-C is an ecohydrological model which couples water and carbon cycles with water use efficiency (WUE) derived from global eddy flux observations. However, a significant limitation of the WaSSI-C model is that it only runs serially. High resolution simulations at a large scale are therefore computationally expensive and cause a run-time memory burden. Using distributed (MPI) and shared (OpenMP) memory parallelism techniques, we revised the original model as dWaSSI-C. We showed that using MPI was effective in reducing the computational run-time and memory use. Two experiments were carried out to simulate water and carbon fluxes over the Australian continent to test the sensitivity of the parallelized model to input data-sets of different spatial resolutions, as well as to WUE parameters for different vegetation types. These simulations were completed within minutes using dWaSSI-C, whereas they would not have been possible with the serial version. The dWaSSI-C model was able to simulate the seasonal dynamics of gross ecosystem productivity (GEP) reasonably well when compared to observations at four eddy flux sites. Sensitivity analysis showed that simulated GEP was more sensitive to WUE during the summer compared to winter in Australia, and woody savannas and grasslands showed higher sensitivity than evergreen broadleaf forests and shrublands. Although our results are model-specific, the parallelization approach can be adopted in other similar ecosystem models for large scale applications. •The WaSSI-C ecohydrological model serially calculates water and carbon fluxes and is therefore restricted to coarse resolution simulations.•MPI and OpenMP techniques significantly reduced computing time allowing for simulations at much higher spatial resolutions.•This approach was demonstrated at a 5 km resolution over the Australian continent, and could be performed in minutes.•The same parallelization approach can be applied to other complex ecohydrological models. |
| Author | Liu, Shirong Sun, Ge Kala, Jatin Liu, Ning Shaikh, Mohsin Ahmed Harper, Richard J. Dell, Bernard |
| Author_xml | – sequence: 1 givenname: Ning orcidid: 0000-0003-0956-3208 surname: Liu fullname: Liu, Ning email: N.Liu@murdoch.edu.au organization: School of Veterinary and Life Sciences, Murdoch University, South Street, Murdoch, WA 6150, Australia – sequence: 2 givenname: Mohsin Ahmed surname: Shaikh fullname: Shaikh, Mohsin Ahmed organization: Pawsey Supercomputing Centre, Perth, WA, Australia – sequence: 3 givenname: Jatin surname: Kala fullname: Kala, Jatin organization: School of Veterinary and Life Sciences, Murdoch University, South Street, Murdoch, WA 6150, Australia – sequence: 4 givenname: Richard J. orcidid: 0000-0003-0268-2917 surname: Harper fullname: Harper, Richard J. organization: School of Veterinary and Life Sciences, Murdoch University, South Street, Murdoch, WA 6150, Australia – sequence: 5 givenname: Bernard surname: Dell fullname: Dell, Bernard organization: School of Veterinary and Life Sciences, Murdoch University, South Street, Murdoch, WA 6150, Australia – sequence: 6 givenname: Shirong surname: Liu fullname: Liu, Shirong organization: Chinese Academy of Forestry, Beijing 10091, China – sequence: 7 givenname: Ge orcidid: 0000-0003-3665-3313 surname: Sun fullname: Sun, Ge organization: U.S. Department of Agriculture, Forest Service, Southern Research Station, Eastern Forest Environmental Threat Assessment Center, Raleigh, NC 27606, USA |
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| SubjectTerms | Annual variations Australia broadleaved evergreen forests Carbon Carbon cycle Computer applications Computer memory Computer simulation data collection Distributed memory Distributed memory parallelism Ecohydrological modeling Ecosystem models ecosystems eddy covariance Environment models Fluxes Grasslands High performance computing Parallel processing Run time (computers) Savannahs savannas Sensitivity analysis Shared memory parallelism shrublands Simulation Spatial data Spatial discrimination summer Vegetation Vortices Water and carbon fluxes Water use Water use efficiency winter |
| Title | Parallelization of a distributed ecohydrological model |
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