Flood Simulation in a Computational Grid

Bringing together flood simulation and grid computing is a matter that has been looked at from several perspectives, as will be shown in this section, e. g. execution and data management, grid services and workflows, parallelization of the numerical model, collaboration, or semantic grid. In fact, hydrodynamic simulation is a classical example of high-performance computing (see Chapter 2), so it is vital to evaluate research endeavors regarding HPC in grid computing environments. Therefore, the approaches with the highest relevance for this study are related to the use of service-oriented grids for an HPC application.

7.2.1. Grid Services and Workflows for Flood Simulation

Few initiatives have previously explored the possibilities of integrating flood simulation services into the grid. In this context, the use of workflow technology promises an automation of complex processes involving those services. Most of the existing results have been achieved by Prof. Ladislav Hluchý¹ and his team in a series of successive research projects: ANFAS (FP5, 2000-2002), CrossGrid (FP5, 2002-2005), MEDIGRID (FP6,2004-2006), K-Wf Grid (FP6,2004-2007), and int.eu.grid (FP6,2006 -2008). Hluchý et al. have investigated workflows and portal-based user interfaces for flood forecasting. Their results are now explained in detail.

Flood Simulation in ANFAS

[HT+02; HT+03; TH04] performed flood simulations and flood damage assessment of the Vah river, Slovakia, in the ANFAS project². A client-server architecture was developed that executed simulations in parallel on a computing cluster.

Flood Forecasting in CrossGrid

Collaboration components were added in the CrossGrid project³ using a Virtual Or-ganization (VO) for flood forecasting. The Virtual OrOr-ganization members included users, data providers (in particular for meteorological input data), storage providers (for simulation outputs), and cycle providers (for computing resources). The only users were hydrological and meteorological experts [HT+04; HA+04]. The FloodGrid

1Slovak Academy of Sciences, Department of Parallel and Distributed Computing

2ANFAS: Data Fusion for Flood Analysis and Decision Support (http://www.ercim.eu/anfas)

3http://www.eu-crossgrid.org

forecasting application consisted of a “cascade” of simulation models ranging from meteorological models over hydrological models to a hydrodynamic model (compare Chapter3, Subsection3.1.2). A simple workflow management system based on Globus Toolkit3and a workflow description language were developed. A workflow consisted of interdependent activities representing parameterized grid jobs. Simulation results could be accessed either via a web-based portal or in a collaborative environment, the “Migrating Desktop”¹ fat client, a pluggable user interface for interactive grid applications [HH+05].

Advancements in MEDIGRID

In the MEDIGRID (Mediterranean Grid of Multi-Risk Data and Models) project new flood forecasting services were implemented as WSRF grid services using Globus Toolkit4. Data transfers had to be done with a low-level transfer service as a replace-ment for GridFTP on the Windows platform. A specialized WSRF job submission service was created that allowed the execution of a pre-configured application locally, on the server, or by submission to a batch system [HH+06].

Semantic Grid Services in K-Wf Grid

The K-Wf Grid project added a user layer based on a knowledge management system that would “learn” from previous user experiences and thus help other users to take best advantage of the grid. A web portal was developed in GridSphere with a Grid Workflow User Interface (GWUI) and a User Assistant Agent (UAA) for sharing and communicating knowledge in the system [HH+06]. A Grid Workflow Execution Service (GWES) controlled the execution of workflows. A complete overview of all components of the workflow system can be found in [BU06]. The flood forecasting cascade is described in [HMH06], [BG+06], and [BH+07]. The hydraulic models integrated into the workflow are the one-dimensional models HEC-RAS and MIKE11.

Interactive Grid Jobs in int.eu.grid

The Interactive European Grid, int.eu.grid, extended the workflow management system from K-Wf Grid (GWES) so that it can make use of an “interactive channel” from a user interface to the workflow manager. int.eu.grid was not strictly conforming to a service-oriented architecture, but was using a pure job submission system. For this reason, the GWES had to be integrated into an executable application that could be

1http://desktop.psnc.pl

submitted as an interactive grid job. The interaction allowed to control the running workflow during its execution, adapt it, and to exchange raw data with an application running in the grid through its standard input and standard output. With these preparations, the flood forecasting application from K-Wf Grid could be ported to int.eu.grid. The flood forecasting workflow was submitted as a grid job and controlled using the GWES interactive channel [SH+08]. As a second use case, an environmental application (particle-based air pollution simulation) was prototypically parallelized and enabled to use the interactive channel to control its parallelism during runtime, i. e.

increase the number of simulated particles to improve precision [SH+08].

7.2.2. Parallel Applications and Services

Floros and Cotronis [FC04] argue that legacy parallel HPC simulation models im-plemented with MPI, such as in meteorology, hydrology, and hydraulics, should be exposed as OGSA grid services, which they call a “virtualization” of the application.

This virtualization step would allow a diversity of clients to be developed without having to dig into the implementation details. Moreover, several of those virtualized MPI simulations could be coupled to simulate flood forecasting scenarios, similar to work done by Hluchý (see above). In [FC04], the authors identified two methods, with which the virtualization could be realized: (1) wrapping the MPI processes or (2) wrapping the data. As the first solution would require the grid service, or part of it, to be located on the process level of the MPI application, this approach was not investigated further. Instead, the second approach was implemented in Globus Toolkit 3. The grid services were designed containing a number of providesanduses quantities, which represent external input and output data items of the application processes, e. g. files, a database, or a data service. Grid service notifications may be used to inform interested clients (e. g. another application process) about changes in a quantity. In this way,uses/providesrelationships could be modeled in a flexible fashion.

It is the services responsibility to wrap and unwrap application data to store outputs into a quantity and deliver them as inputs to the corresponding process.

In a subsequent publication [FC06], the same authors developed the “ServOSims”

framework for data-centric composition of service-oriented simulations with WSRF grid services and Globus Toolkit4. The application was provided as a stateful WS-Resource with a^runoperation,providesandusesquantities were implemented as input and output WS-ResourceProperties, and the notification mechanism from [FC04] was replaced by WS-Notification. The notifications allowed a data-centric workflow composition of simulations via input and output file resources. If necessary, an intermediate grid service transformed the exchanged data on the way. The grid service implementation actually probed the applications’s standard input and output streams for data exchange

with the application, in order to receive information from and give simple instructions to the application. This approach is very similar to the interactive channel described in [SH+08].

7.2.3. Real-World Hydrodynamic Models for Flood Simulation

Particular hydrodynamic models for flood simulation differ in their support of high-performance computing architectures and parallel or distributed computing. There is an overview of parallelization methods for2D flood models in [NF+10]. The following list contains the most important flow models, which are accepted in hydraulic engineering practice and backed by an unstructured, two-dimensional discretization¹, and gives details about their degree of parallelization, e. g. usage of the MPI or OpenMP libraries (see Chapter2, Subsection2.1.3).