This is a project of Dr. Killian Schwarz of GSI (Helmholtzzentrum für Schwerionenforschung GmbH or Helmholtz Centre for Heavy Ion Research) that is being supported/endorsed by UPAA Germany. The GSI is building the FAIR (Facility for Antiproton and Ion Research), one of the biggest projects worldwide and will be the counterpart of CERN in Germany. FAIR will therefore complement CERN’s work. The experiments in CERN, and also later those in FAIR are dealing with the search for the ultimate nature of the material world. Now, processing and storing data from these experiments will require enormous computing power, and will therefore utilize the concept of Grid Computing. Within this context, PANDA Grid (Proton Anti-proton Darmstadt Grid) was established in 2003. The PANDA Grid was established to support the computational needs of the PANDA experiment, one of the 2 big FAIR experiments.
UPAA Germany helps in linking possible collaborators from the Philippines to this worldwide PANDA Grid project. To date, there are already a number of countries connected with the grid, and Dr. Schwarz wanted that the grid be expanded in South East Asia, especially in the Philippines. Specifically, he is contacting universities/institutions that might be interested to join the grid. To be connected, the institution need only at least one computer (although more would be better). Note that the institution doesn’t need to be doing anything physics-related since it does not need to join the FAIR collaboration. However, if it wants and has the capacity to do so, then it can also join the FAIR project. Those who would benefit most from the PANDA Grid collaboration are the scientific/technical and computing institutions.
Among the most important advantages of joining the grid are as follows:
- Gaining of experience in Grid Computing and other new technologies;
- Visibility in a global research community (FAIR); and
- Making use of the enormous computing power of the grid for their own computational needs (e.g., earthquake simulation, weather forecasting, etc.).
For more information about the PANDA Grid, you can access: http://panda-wiki.gsi.de/cgi-bin/view/Computing/PandaGrid and http://serpiero.to.infn.it/map.jsp