The purpose of the Institute of Applied Computing and Community Code (IAC3) of the University of the Balearic Islands (UIB) is to contribute to the interdisciplinary science of scientific and commercial simulation. As part of this contribution, the IAC3 develops open source code. GridSystems is proudly contributing financially and technically to sustain the IAC3. GridSystems' CTO, Dr. Antonio Arbona, is the Deputy Manager of the Institute.

The Technical University of Valencia (Spain) has a Campus Grid that is open to all the research groups in the university. The High Performance Networking and Computing Group (GRyCAP) is the name of the research group at the Valencian University of Technology (UPV) led by Professor Vicente Hernández García. The group consists of 28 members devoted to research efforts that have been collaborating with enterprises for several years.

The University Carlos III in Madrid has got a Campus Grid so that the advantages of Fura are available for all the research groups. For example, the Department of Signal Theory has several research groups led by Prof. Anibal Figueiras, with around 50 staffed researchers, that are involved in many grid projects. One of them, for instance, uses Matlab for the analysis of electromagnetic signals.Fura is able to distribute the Matlab functions created by the users. Distributing the computations among a large number of connected machines allows reducing the execution time; moreover, the fault-tolerant design of Fura ensures that the execution is always performed. The fact that they could share and optimize their resources was a key factor to decide in favor of the implementation of our platform.

A cluster of research groups, and around 16 researchers at the Pompeu Fabra University, led by Professor Alex Frangi, work on image analysis and computer vision in the area of facial biometry and computational analysis of cardiovascular medical images. The image processing algorithms are hungry in terms of processing power. With Fura, the calculations are distributed across the idle computers in the network of the University, notably shrinking the amount of time needed to obtain results. In cardiovascular imaging this methodology could be soon used to analyze the patient’s data in real time, which would improve the survival rates during critical episodes