Parallel Control Flow

The Gaja3Dpar Grid-WPS monitors the submitted grid job, updates its WPS status document, and waits for completion. When done, the GridFTP resource locations of the process results are stored in the respectiveGaja3DResourceproperties and in the status document. The service then reports either success or failure. The WS-Resource

is not destroyed at this point, so that it is possible to send subsequent requests to the same instance of Gaja3Dpar, e. g. with the goal of chaining the raster creation and breakline detection steps for a tile. It is foreseen that aGaja3DResourcedoes not only manage results by linking to them in a resource property, but it could also store them as OGSA resources. Dorka [Dor09] has investigated this possibility. However, the question remains how the actual payload data could be stored more efficiently, e. g. in a transactional WFS or WCS backed by a database.

Figure6.11.:Flow model discretization control flow with parallel region.

It was shown above how a partitioning of the domain boundary leads to tiles for which breaklines can be detected independently. The control flow diagram (Figure6.11) includes the partitioning step as the first activity. The decision to start with either point or raster data is actually made at the beginning of the workflow and is thus constant for all tiles. For each tile a separate breakline detection process is initiated and executed in a parallel region. It consists of a data acquisition step and either one or two Gaja3Dpar

operations (Create Raster,Detect Breaklines). The breakline results of all tiles are joined at the end of the parallel region and merged prior to the final Gaja3Dpar mesh creation step,^{Create Tin}.

Conclusion

The parallel process for mesh generation and the prototypical implementation of the flow model discretization service described in this chapter demonstrated that massive amounts of digital terrain data can efficiently be processed in the domain of hydrodynamic modeling using grid technology. For the duration of the GDI-Grid project that partially funded this thesis, the service and a flow model discretization workflow had successfully been deployed in the German D-Grid infrastructure¹. Furthermore, the presented abstractions for service development allow for modularity, reuse, and interoperability of geoprocessing services in a spatial data infrastructure.

By conforming to geoprocessing and grid standards — according to the described Grid-WPS framework — the geoprocessing functions and the grid workflow can be accessed by both WPS and grid clients with a valid grid certificate. In this way, the meshing software for flow model discretization has been made available as a distributed geoprocessing service to all users of a virtual organization for geoprocessing.

Yet, the service is not restricted to an application in the domain of hydrodynamic modeling. The operations may also be used separately or as part of other scientific workflows. Meshing a bounded domain is an operation that needs to be performed when creating unstructured discretizations for numerical models in other disciplines, as well, such as aerodynamics or structural mechanics. As another example, the rasterization of unstructured point clouds is a typical operation required in remote sensing applications. Finally, breakline detection together with mesh creation may also aid in data reduction and improved three-dimensional visualization of large terrain data sets.

1After the project ended, the respective infrastructure was suspended due to lack of funding.

The flood simulation service developed in this chapter shows how flood scenarios can be explored by two-dimensional numerical simulation of the flooding process using an existing flow model discretization. This is done by developing a standard-compliant Grid-WPS for flood simulation, following the gridification method demonstrated in Chapter5, and by extension of a numerical flow model for parallel execution in the grid.

The Grid-WPS makes it possible to integrate the flood simulation service into a spatial data infrastructure in the form of a geoprocessing service. In this way, the service is enabled to request input for the hydrodynamic simulation dynamically from sensor observation services delivering flood hydrograph data. The Grid-WPS further executes and manages the hydrodynamic simulation in a way that utilizes the power of the provided distributed high-performance computing environment. The flood simulation service follows up on the flow model discretization service (Chapter 6) in that the created flow model can now be set up for the simulation and evaluation of flood scenarios in a computational grid. This adds support for the processing phase of hydrodynamic modeling. This has been evaluated through test runs on grid resources of the German D-Grid infrastructure.

The structure of this chapter is as follows: Section7.1briefly presents the motivations for developing a flood simulation service and lists the objectives and requirements of the prototype. In Section 7.2an overview of the current state of research regarding flood simulation in a grid computing environment is given. The architectural design of the Grid-WPS prototype, its interface, as well as the parallelization of a hydrody-namic numerical model based on the Kalypso Simulation Platform is described in Section7.3.

7.1. Introduction

In times of climate change, increasing flood risk, and the enactment of flood man-agement policies and plans, detailed analyses of the dynamics of flooding and the creation of flood maps have become indispensable. There is a growing demand for the

reliable assessment of forthcoming flood events and thus the need for two-dimensional simulation of river flow and floodplain inundation, urban flooding, and storm surges capturing the course of a flood event with a high spatial and temporal resolution. Such simulation results are used in flood mapping and flood risk management, with the purpose of obtaining a basis for informing stakeholders and the population prior to new flood disasters about the current flood risk as well as possible future impacts, e. g.

due to climate change. In this way, flood simulation can help to improve emergency plans and reduce the consequences of flooding [ZA+10].