Keywords: grid computing, grid applications, cloud computing, cloud applications, engineering applications, numerical algorithms, data management, DEISA project.

This article presents a review on engineering and scientific application simulations on compute clusters, grids and clouds. It analyzes different categories of complex engineering algorithms, data management, the need for performance optimization, and their suitability for grids and for clouds. Algorithm issues can have a strong impact on the effort and success of porting engineering applications to grids and clouds. Often, the resulting performance of the application will suffer, as well as the user-friendly access to the resources, the grid or cloud services, the application, the data, and the final processing results.

In the recent past, hundreds of engineering applications have been ported to grids and clouds. Each application is unique in that it solves a specific problem, based on modelling a specific phenomenon in nature (physics, chemistry, biology, etc.), and is presented as a mathematical formula together with appropriate initial and boundary conditions. This is usually represented by its discrete analogue using sophisticated numerical methods, 'translated' into a programming language computers can understand, adjusted to the underlying computer architecture, embedded in a workflow, and accessible remotely by the user through a secure, transparent and application-specific portal. These few words summarize the wide spectrum and complexity we face in engineering problem solving on grid and cloud infrastructures.

The user (and especially the developer) faces several layers of complexity when porting applications to a computing environment, especially to a compute or data grid of distributed networked nodes ranging from desktops to supercomputers. These nodes usually consist of several to many loosely or tightly coupled processors and, more and more, these processors contain few to many cores. To run efficiently on such systems, applications have to be adjusted to the different system layers, taking into account different levels of granularity, from fine-grain structures deploying multi-core architectures at processor level, to the coarse granularity found in application workflows representing for example multi-physics applications, such as fluid structure interaction. In addition the user has to take into account the specific requirements of the grid coming from the different components of the grid services architecture, such as security, resource management, information services, and data management.

As a useful case scenario for applications in the grid, the EU funded DEISA project is discussed. Finally, roadblocks such as sensitive data, security concerns, licensing issues, and intellectual property, are mentioned.

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