the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Submitted as: development and technical paper 27 Jan 2021
Submitted as: development and technical paper | 27 Jan 2021
GP-SWAT (v1.0): a two-layer graph-based parallel simulation framework for the SWAT model
- 1Department of Resources and Environmental Sciences, Quanzhou Normal University, Donghai Street 398, Quanzhou, Fujian 362000, China
- 2College of Computer and Information Engineering, Xiamen University of Technology, Ligong Road 600, Xiamen, Fujian 361024, China
- 3Digital Fujian Institute of Big Data for Natural Hazards Monitor, Ligong Road 600, Xiamen, Fujian 361024, China
- 4School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing 210000, China
- These authors contributed equally to this work.
- 1Department of Resources and Environmental Sciences, Quanzhou Normal University, Donghai Street 398, Quanzhou, Fujian 362000, China
- 2College of Computer and Information Engineering, Xiamen University of Technology, Ligong Road 600, Xiamen, Fujian 361024, China
- 3Digital Fujian Institute of Big Data for Natural Hazards Monitor, Ligong Road 600, Xiamen, Fujian 361024, China
- 4School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing 210000, China
- These authors contributed equally to this work.
Abstract. High-fidelity and large-scale hydrological models are increasingly used to investigate the impacts of human activities and climate change on water availability and quality. However, the detailed representations of real-world systems and processes contained in these models inevitably lead to prohibitively high execution times, ranging from minutes to days. This becomes computationally prohibitive or even infeasible when large iterative model simulations are involved. In this study, we propose a generic two-layer model parallelization scheme to reduce the run time of computationally expensive model applications through a combination of model spatial decomposition and the graph-parallel Pregel algorithm. Taking the Soil and Water Assessment Tool (SWAT) as an example, we implemented a generic tool named GP-SWAT, enabling model-level and subbasin-level model parallelization on a Spark computer cluster. We then evaluated GP-SWAT in two sets of experiments to demonstrate the potential of GP-SWAT to accelerate single and iterative model simulations and to run in different environments. In each test set, Spark-SWAT was applied for the parallel simulation of eight synthetic hydrological models with different input/output (I/O) burdens and river network characteristics. The experimental results indicate that GP-SWAT can effectively solve high-computational-demand problems of the SWAT model. In addition, as a scalable and flexible tool, it can be run in diverse environments, from a commodity computer running the Microsoft Windows operating system to a Spark cluster consisting of a large number of computational nodes. Moreover, it is possible to apply this generic scheme to other subbasin-based hydrological models or even acyclic models in other domains to alleviate input/output (I/O) demands and optimize model computational performance.
Dejian Zhang et al.
Status: closed
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RC1: 'Comment on gmd-2020-429', Anonymous Referee #1, 17 Mar 2021
This manuscript presents a two-layer graph-based parallel simulation framework for the SWAT model, which can provide valuable reference for watershed management. I suggest the manuscript can be accepted after major revision.
1. The specific results of the parallel-computing performance should be presented in the abstract.
2. Line 95: It's better to state why this research want to propose a new parallelization scheme?
3. Section 2.1 should be integrated to the introduction part, which can help to figure out what is the missing part of existing researches.
4. Fig. 3: it is not convinect for most reader to read the codes. Some diagrams are needed to express the same meanings.
5. Fig 5: How can the actural speedup ratios larger than te theoretical ones?
6. Line 230: some details of the study areas should be added.
7. Line 235: it seems that the computation amount is not very large in the case study. I wonder how the proposed parallel computing system performs for the applications with many different amount of simulation units. -
RC2: 'Comment on gmd-2020-429', Liangjun Zhu, 13 May 2021
General comments:
This manuscript proposed a unified parallelization strategy for both watershed-level and subbasin-level parallelization and developed the GP-SWAT accordingly, which is valuable and useful for both developers and users in the scientific community. Except for the specific comments posted by Anonymous Referee #1 (https://gmd.copernicus.org/preprints/gmd-2020-429#RC1), I have some other specific comments.Specific comments:
1. The phase “two-layer graph-based parallelization” is ambiguous and inexact. (1) Do you mean “layer” equals “level” since you use “model-level” (or "watershed-level", please unify the terms) and “subbasin-level” in the manuscript? (2) The “graph-based” is the parallelization strategy for “subbasin-level” not for “model-level”, since model runs are independent to each other and only their outputs are concerned.
2. In the introduction, the first paragraph stated that both the single model and numerous models require prohibitive execution time. However, the first three of the four methods introduced in the second paragraph are used for model-level applications. In my view, it is more clear to introduce the methods for alleviating computational burden for the model level applications and the single model, separately. And then both focus on parallel computing.
3. In the sentence around No 65, it is more precisely to cite Liu et al. (2016) and/or Zhu et al. (2019) rather than Liu et al. (2014).
4. In the sentence around No 80, “However, these methods have two major limitations: … complex computational facilities that may not be readily available…” should be reconsidered. In fact, many parallel computing models (e.g., MPI) can also be running on the personal computer and obtain a good speedup ratio.
5. This manuscript focuses on the parallelization of both model-level and subbasin-level but missed existing similar methods such as Zhu et al. (2019).
6. Overall, the introduction failed to raise the scientific issue clearly and precisely, that is, there is no unified parallelization strategy for both watershed-level and subbasin-level parallelization that do not need to reconstruct source code of hydrologic model to handle data communication among subbasins explicitly (e.g., using MPI). If this is correct, the title may also be changed accordingly.
7. What is the phrase “the current computation step” mean in the sentence around No 140? To my understanding, the proposed method is different from the spatial-temporal discretization proposed by Wang et al. (2013). For example, in Fig 4, Subbasin 1 and Subbasin 2 are executed for the entire simulation period (e.g., 5 years) first, then Subbasin 3 begins to run, and so on. This may lead to a poor load balance, and hence a low speed up ratio, especially for a single model run. Is this right?
8. In Section 4.1, all the results are compared through speed-up ratios, you should also give the actual execution times. I want to know the performance of the subbasin-level parallelization for a single model simulation compared with the original SWAT model.
9. Why use two study areas? From my perspective, the two case studies have no significant difference. We need more information about the study areas.
10. In sentences around No 235, it is weird that the numbers of HRUs per subbasin can be set the same.
Technical corrections:
1. Did you mean that the “Spark-SWAT” is the alias of the “GP-SWAT”?
2. In the code, all file paths are specific to the author’s computer. This is not suitable for code distribution. Even so, the modification of these paths should be clarified in the tutorial.References:
Liu, J., Zhu, A.-X., Qin, C.-Z., Wu, H., and Jiang, J.: A two-level parallelization method for distributed hydrological models, Environmental Modelling & Software, 80, 175–184, https://doi.org/10.1016/j.envsoft.2016.02.032, 2016.
Wang, H., Fu, X., Wang, Y., and Wang, G.: A High-performance temporal-spatial discretization method for the parallel computing of river basins, Computers & Geosciences, 58, 62–68, https://doi.org/10.1016/j.cageo.2013.04.026, 2013.
Zhu, L.-J., Liu, J., Qin, C.-Z., and Zhu, A.-X.: A modular and parallelized watershed modeling framework, Environmental Modelling & Software, 122, 104526, https://doi.org/10.1016/j.envsoft.2019.104526, 2019.
-
AC1: 'Comment on gmd-2020-429', Qiaoying Lin, 03 Jun 2021
The comment was uploaded in the form of a supplement: https://gmd.copernicus.org/preprints/gmd-2020-429/gmd-2020-429-AC1-supplement.pdf
Status: closed
-
RC1: 'Comment on gmd-2020-429', Anonymous Referee #1, 17 Mar 2021
This manuscript presents a two-layer graph-based parallel simulation framework for the SWAT model, which can provide valuable reference for watershed management. I suggest the manuscript can be accepted after major revision.
1. The specific results of the parallel-computing performance should be presented in the abstract.
2. Line 95: It's better to state why this research want to propose a new parallelization scheme?
3. Section 2.1 should be integrated to the introduction part, which can help to figure out what is the missing part of existing researches.
4. Fig. 3: it is not convinect for most reader to read the codes. Some diagrams are needed to express the same meanings.
5. Fig 5: How can the actural speedup ratios larger than te theoretical ones?
6. Line 230: some details of the study areas should be added.
7. Line 235: it seems that the computation amount is not very large in the case study. I wonder how the proposed parallel computing system performs for the applications with many different amount of simulation units. -
RC2: 'Comment on gmd-2020-429', Liangjun Zhu, 13 May 2021
General comments:
This manuscript proposed a unified parallelization strategy for both watershed-level and subbasin-level parallelization and developed the GP-SWAT accordingly, which is valuable and useful for both developers and users in the scientific community. Except for the specific comments posted by Anonymous Referee #1 (https://gmd.copernicus.org/preprints/gmd-2020-429#RC1), I have some other specific comments.Specific comments:
1. The phase “two-layer graph-based parallelization” is ambiguous and inexact. (1) Do you mean “layer” equals “level” since you use “model-level” (or "watershed-level", please unify the terms) and “subbasin-level” in the manuscript? (2) The “graph-based” is the parallelization strategy for “subbasin-level” not for “model-level”, since model runs are independent to each other and only their outputs are concerned.
2. In the introduction, the first paragraph stated that both the single model and numerous models require prohibitive execution time. However, the first three of the four methods introduced in the second paragraph are used for model-level applications. In my view, it is more clear to introduce the methods for alleviating computational burden for the model level applications and the single model, separately. And then both focus on parallel computing.
3. In the sentence around No 65, it is more precisely to cite Liu et al. (2016) and/or Zhu et al. (2019) rather than Liu et al. (2014).
4. In the sentence around No 80, “However, these methods have two major limitations: … complex computational facilities that may not be readily available…” should be reconsidered. In fact, many parallel computing models (e.g., MPI) can also be running on the personal computer and obtain a good speedup ratio.
5. This manuscript focuses on the parallelization of both model-level and subbasin-level but missed existing similar methods such as Zhu et al. (2019).
6. Overall, the introduction failed to raise the scientific issue clearly and precisely, that is, there is no unified parallelization strategy for both watershed-level and subbasin-level parallelization that do not need to reconstruct source code of hydrologic model to handle data communication among subbasins explicitly (e.g., using MPI). If this is correct, the title may also be changed accordingly.
7. What is the phrase “the current computation step” mean in the sentence around No 140? To my understanding, the proposed method is different from the spatial-temporal discretization proposed by Wang et al. (2013). For example, in Fig 4, Subbasin 1 and Subbasin 2 are executed for the entire simulation period (e.g., 5 years) first, then Subbasin 3 begins to run, and so on. This may lead to a poor load balance, and hence a low speed up ratio, especially for a single model run. Is this right?
8. In Section 4.1, all the results are compared through speed-up ratios, you should also give the actual execution times. I want to know the performance of the subbasin-level parallelization for a single model simulation compared with the original SWAT model.
9. Why use two study areas? From my perspective, the two case studies have no significant difference. We need more information about the study areas.
10. In sentences around No 235, it is weird that the numbers of HRUs per subbasin can be set the same.
Technical corrections:
1. Did you mean that the “Spark-SWAT” is the alias of the “GP-SWAT”?
2. In the code, all file paths are specific to the author’s computer. This is not suitable for code distribution. Even so, the modification of these paths should be clarified in the tutorial.References:
Liu, J., Zhu, A.-X., Qin, C.-Z., Wu, H., and Jiang, J.: A two-level parallelization method for distributed hydrological models, Environmental Modelling & Software, 80, 175–184, https://doi.org/10.1016/j.envsoft.2016.02.032, 2016.
Wang, H., Fu, X., Wang, Y., and Wang, G.: A High-performance temporal-spatial discretization method for the parallel computing of river basins, Computers & Geosciences, 58, 62–68, https://doi.org/10.1016/j.cageo.2013.04.026, 2013.
Zhu, L.-J., Liu, J., Qin, C.-Z., and Zhu, A.-X.: A modular and parallelized watershed modeling framework, Environmental Modelling & Software, 122, 104526, https://doi.org/10.1016/j.envsoft.2019.104526, 2019.
-
AC1: 'Comment on gmd-2020-429', Qiaoying Lin, 03 Jun 2021
The comment was uploaded in the form of a supplement: https://gmd.copernicus.org/preprints/gmd-2020-429/gmd-2020-429-AC1-supplement.pdf
Dejian Zhang et al.
Data sets
Test models used to assess the performance of GP-SWAT Dejian Zhang, Bingqing Lin, Jiefeng Wu, and Qiaoying Lin https://doi.org/10.5281/zenodo.4447969
Model code and software
Source code of GP-SWAT Dejian Zhang, Bingqing Lin, Jiefeng Wu, and Qiaoying Lin https://doi.org/10.5281/zenodo.4447969
Dejian Zhang et al.
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