Using Open Source Desktop Grids in Scientific Computing and Visualization

Scientific Computing is the collection of tools, techniques, and theories required to develop and solve on a computer, mathematical models of problems in science and engineering, and its main goal is to gain insight of such problems (Heat, 2002; Steeb et al., 2004; Hamming, 1987). Generally, it is difficult to understand or communicate information from complex or large datasets generated by scientific computing methods and techniques (computational simulations, complex experiments, observational instruments etc.). Therefore, support of Scientific Visualization is needed, to provide techniques, algorithms, and software tools that are necessary to extract and display properly important information from numerical data. Complex computational and visualization algorithms normally require large amounts of computational power. The computing power of a single desktop computer is not sufficient for running such complex algorithms, and, traditionally, large parallel supercomputers or dedicated clusters were used for this job. However, very high initial investments and maintenance costs limit the large-scale availability of such systems. A more convenient solution, which is becoming more and more popular, is based on the use of non-dedicated desktop PCs in a desktop grid computing environment. Harnessing idle CPU cycles, storage space and other resources of networked computers, to work together, on a particularly computational intensive application, perform this job. Increasing power and communication bandwidth of desktop computers provides for this solution as well. In a Desktop Grid (DG) system, the execution of an application is orchestrated by a central scheduler node, which distributes the tasks amongst the worker nodes and awaits workers' results. It is important to note that an application only finishes when all tasks have been completed. The attractiveness of exploiting desktop grid systems is further reinforced by the fact that costs are highly distributed: every volunteer supports her resources (hardware, power costs and Internet connections), while the benefited entity provides management infrastructures, namely network bandwidth, servers and management services, receiving in exchange a massive and otherwise unaffordable computing power. The typical and most appropriate application for desktop grid comprises independent tasks (no communication exists amongst tasks) with a high computation to communication ratio (Domingues, Silva & Silva, 2006; Constantinescu, 2008). The usefulness of desktop grid computing is not limited to major high throughput public computing projects. Many institutions, ranging from