Earthquake and Tsunami Workflow Leveraging the Modern HPC/Cloud Environment in the LEXIS Project
Accurate and rapid earthquake loss assessments and tsunami early warnings are critical in modern society to allow for appropriate and timely emergency response decisions. In the LEXIS project, we seek to enhance the workflow of rapid loss assessments and emergency decision support systems by leveraging an orchestrated heterogeneous environment combining high-performance computing resources and Cloud infrastructure. The workflow consists of three main applications: firstly, after an earthquake occurs, its shaking distribution (ShakeMap) is computed based on the OpenQuake code. Secondly, if a tsunami may have been triggered by the earthquake, tsunami simulations (first a fast and coarse and later a high-resolution and computationally intensive analysis) are performed based on the TsunAWI simulation code that allows for an early warning in potentially affected areas. Finally, based on the previous results, a loss assessment based on a dynamic exposure model using open data such as OpenStreetMap is performed. To consolidate the workflow and ensure respect of the time constraints, we are developing an extension of a time-constrained dataflow model of computation, layered above and below the workflow management tools of both the high-performance computing resources and the Cloud infrastructure. This model of computation is also used to express tasks in the workflow at the right granularity to benefit from the data management optimisation facilities of the LEXIS project. This paper describes the workflow, the associated computations and the model of computation within the LEXIS platform.
This work was supported by the LEXIS project - the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825532.
- 1.Terzo, O., Walter E., Levrier, M., Hachinger, S., Magarielli, D., Goubier, T., Louise, S., Parodi, A., Murphy, S., D’Amico, C., Ciccia, S., Danovaro, E., Lagasio, M., Donnat, F., Golasowski, M., Quintino, T., Hawkes, J., Martinovic, T., Riha, L., Slaninova, K., Serra, S., Peveri, R., Scionti, A., Martinovic J.: HPC, cloud and big-data convergent architectures: the lexis approach. In: Conference on Complex, Intelligent, and Software Intensive Systems (CISIS) (2019)Google Scholar
- 3.Cima, V., Böhm, S., Martinovič, J., Dvorský, J., Janurová, K., Vander Aa, T., Ashby, T.J., Chupakhin, V.: HyperLoom: a platform for defining and executing scientific pipelines in distributed environments. In: Proceedings of the 9th Workshop and 7th Workshop on Parallel Programming and RunTime Management Techniques for Manycore Architectures and Design Tools and Architectures for Multicore Embedded Computing Platforms, PARMA-DITAM 2018, pp. 1–6. ACM, New York (2018)Google Scholar
- 4.Do, X., Louise, S., Cohen, A.: Transaction parameterized dataflow: a model for context-dependent streaming applications. In: 2016 Design, Automation & Test in Europe Conference & Exhibition, DATE 2016, Dresden, Germany, 14–18 March 2016, pp. 960–965 (2016)Google Scholar
- 7.Gautier, T., Besseron, X., Pigeon, L.: KAAPI: a thread scheduling runtime system for data flow computations on cluster of multi-processors. In: Proceedings of the 2007 International Workshop on Parallel Symbolic Computation, PASCO 2007, pp. 15–23. ACM, New York (2007)Google Scholar
- 8.Griffin, J., Latief, H., Kongko, W., Harig, S., Horspool, N., Hanung, R., Rojali, A., Maher, N., Fuchs, A., Hossen, J., Upi, S., Dewanto, S.E., Rakowsky, N., Cummins, P.: An evaluation of onshore digital elevation models for modeling tsunami inundation zones. Front. Earth Sci. 3, 32 (2015)CrossRefGoogle Scholar
- 10.Louise, S., Dubrulle, P., Goubier, T.: A model of computation for real-time applications on embedded manycores. In: 2014 IEEE 8th International Symposium on Embedded Multicore/Manycore SoCs (MCSoc), pp. 333–340, September 2014Google Scholar
- 12.Rudloff, A., Lauterjung, J., Münch, U. (eds.): The GITEWS Project (German-Indonesian Tsunami Early Warning System). NHESS - Special Issues. Copernicus Publications, Göttingen (2009)Google Scholar
- 13.Schorlemmer, D., Beutin, T., Hirata, N., Wyss, M., Cotton, F., Prehn, K.: Global dynamic exposure and the OpenBuildingMap - communicating risk and involving communities. In: EGU General Assembly Conference Abstracts, volume 20 of EGU General Assembly Conference Abstracts, p. 12871, April 2018Google Scholar
- 14.Shewchuk, J.R.: Triangle: engineering a 2D quality mesh generator and delaunay triangulator. In: Lin, M.C., Manocha, D. (eds.) Applied Computational Geometry: Towards Geometric Engineering, volume 1148 of Lecture Notes in Computer Science, pp. 203–222. Springer, Heidelberg (1996). From the First ACM Workshop on Applied Computational GeometryCrossRefGoogle Scholar
- 15.Voigt, S., Giulio-Tonolo, F., Lyons, J., Kučera, J., Jones, B., Schneiderhan, T., Platzeck, G., Kaku, K., Hazarika, M.K., Czaran, L., Li, S., Pedersen, W., James, G.K., Proy, C., Muthike, D.M., Bequignon, J., Guha-Sapir, D.: Global trends in satellite-based emergency mapping. Science 353(6296), 247–252 (2016)CrossRefGoogle Scholar