**1. Introduction**

26 Grid Computing

246 Grid Computing – Technology and Applications, Widespread Coverage and New Horizons

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for Pairwise Testing, *Proceedings of the 12th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems - KES 2008*, Vol. 5177 of *Lecture Notes in Computer Science*, Springer-Verlag, pp. 493–500. http://dx.doi.org/10. The accurate and efficient solution of Maxwell's equation is the problem addressed by the scientific discipline called Computational ElectroMagnetics (CEM). Many macroscopic phenomena in a great number of fields are governed by this set of differential equations: electronic, geophysics, medical and biomedical technologies, virtual EM prototyping, besides the traditional antenna and propagation applications. Therefore, many efforts are focussed on the development of new and more efficient approach to solve Maxwell's equation. The interest in CEM applications is growing on. Several problems, hard to figure out few years ago, can now be easily addressed thanks to the reliability and flexibility of new technologies, together with the increased computational power. This technology evolution opens the possibility to address large and complex tasks. Many of these applications aim to simulate the electromagnetic behavior, for example in terms of input impedance and radiation pattern in antenna problems, or Radar Cross Section for scattering applications. Instead, problems, which solution requires high accuracy, need to implement full wave analysis techniques, e.g., virtual prototyping context, where the objective is to obtain reliable simulations in order to minimize measurement number, and as consequence their cost. Besides, other tasks require the analysis of complete structures (that include an high number of details) by directly simulating a CAD Model. This approach allows to relieve researcher of the burden of removing useless details, while maintaining the original complexity and taking into account all details. Unfortunately, this reduction implies: (a) high computational effort, due to the increased number of degrees of freedom, and (b) worsening of spectral properties of the linear system during complex analysis. The above considerations underline the needs to identify appropriate information technologies that ease solution achievement and fasten required elaborations. The authors analysis and expertise infer that Grid Computing techniques can be very useful to these purposes. Grids appear mainly in high performance computing environments. In this context, hundreds of off-the-shelf nodes are linked together and work in parallel to solve problems, that, previously, could be addressed sequentially or by using supercomputers. Grid Computing is a technique developed to elaborate enormous amounts of data and enables large-scale resource sharing to solve problem by exploiting distributed scenarios. The main advantage of Grid is due to parallel computing, indeed if a problem can be split in smaller tasks, that can be executed independently, its solution calculation fasten up considerably. To exploit this advantage, it is necessary to identify a technique able to split original electromagnetic task into a set of smaller subproblems. The Domain Decomposition (DD) technique, based on the block generation algorithm introduced in Matekovits et al.

Fig. 1. A fighter discretized with 4774 triangles is subdivided into 4 blocks, shown with

in research and industry, representing more than 400 organizations in 50 countries.

Grid Computing is often described by referring to the analogy between electrical networks and grid. When people access to electric network they use wall sockets with no care about where or how electricity is actually generated. This relation underlies that computing becomes pervasive thanks to Grid Computing diffusion. Therefore, individual users (or client applications) can access computing resources (processors, storage, data, applications, etc..) when needed with little or no knowledge about where those resources are located or what underlying technologies, hardware, operating system are used. A further definition is given by "Grid is an infrastructure that involves the integrated and collaborative use of

The rapid technology growth in the recent years has helped the development and rapid expansion of Grid Computing. "The roots of Grid Computing can be traced back from the late 80s when the research about scheduling algorithms for intensive applications in distributed environments accelerated considerably" Kourpas (2006). In the late 1990s began to emerge, a more generalized framework for accessing high performance computing systems, and at the turn of the millennium, the pace of change was accelerated by the recognition of potential synergies between the grid and the emerging Service Oriented Architectures (SOA) through the creation of the Open Grid Services Architecture (OGSA) Kourpas (2006). "The Open Grid Services Architecture is a set of standards defining the way in which information is shared among several components of large, heterogeneous grid systems. The OGSA is, in effect, an extension and refinement of the Service Oriented Architecture" OGSA (2007). The Open Grid Services Architecture is a standard created by the Open Grid Forum (OGF) OGF (2011). OGF was founded in 2006 from the Global Grid Forum (GGF) and the Enterprise Grid Alliance (EGA). GGF had a rich background and established international presence within the academic and research communities while EGA was focused on developing and promoting enterprise grid solutions. The OGF community now counts thousands of members working

Grid Infrastructure for Domain Decomposition Methods in Computational ElectroMagnetics 249

different colors.

**3. Background**

**3.1 Grid Computing technologies**

(2007) and Francavilla et al. (2011), perfectly addresses our requirements (see Section 3.4 for details). In this chapter, a Grid Computing infrastructure is presented. This architecture allows parallel block execution by distributing tasks to nodes that belong to the Grid. The set of nodes is composed by physical machines and virtualized ones. This feature enables great flexibility and increase available computational power. Furthermore, the presence of virtual nodes allows a full and efficient Grid usage, indeed the presented architecture can be used by different users that run different applications.

The chapter is organized as follow, Section 2 briefly explains author's contribution. Section 3 describes technologies used and summarized Domain Decomposition principles. The architecture is shown in Section 4, while the advantages derived by its adoption are illustrated in Section 5 and Section 6 draws conclusions and gives hints for future works.
