The Center for Instrument Sharing of the University of Pisa (CISUP) coordinates management and access for a wide range of Core Facilities for physical science researchers. Gathering several hundreds of faculty members and technical staff from 18 Departments of the University of Pisa, CISUP supports our research, education and services at national and international level by:
• Designing and developing large analytical instrumentation and research infrastructures across the University of Pisa;
• Offering our students and faculty members access to state-of-art analytical laboratories and infrastructures;
• Creating and managing networks of existing laboratories within the university, enabling easy fruition, as well as continuous upgrade and development;
• Promoting the accreditation of the laboratories in compliance with EU Regulation 765/2008.
Check out the research facilities of CISUP hosted by the Department of Physics and INFN:
Green Data Center
The Green Data Centre of the University of Pisa is a next-generation Data Centre facility built in San Piero a Grado (Pisa), launched in late 2016. It was built using the latest available technologies for cooling facilities, power supply and distribution, reaching a record PUE (Power Used Effectiveness) of 1.15 / 1.2. It occupies 250 m2 and its computing rooms host several tens of racks equipped with specific power supply circuits and in-row cooling facilities. The Green Data Centre hosts hundreds of last-generation servers and 6 petabytes of storage for scientific computing (HPC Clusters, GPU servers, Multiprocessor Nodes) and virtualization services (self-service virtual machine systems and virtual desktops). The networking is based on the latest generation Ethernet switching and Infiniband/Omnipath for HPC Services. The University Network is reached by a 200 Gb/s fibre network, and the GARR Network by a 100 Gb/s link. The Computing@Unipi service address the computing resources provided by the Data Centre to students and faculty members of the University of Pisa who need to perform intensive scientific computation by HPC clusters (with or without GPU) or to use appropriately configured autonomous virtual machines.