New GPU Computing Collaboration Network for Scientists, Researchers, and Developers Launches at SC09

The Coordinated Science Laboratory, home of the first CUDA Center of Excellence, today announced the launch of a new network of research communities designed to foster collaboration among scientists, researchers, and developers utilizing GPU computing.

The GPU Computing Collaboration Network was announced at SC09 in Portland, Oregon.

Members of this network will communicate with each other within and across communities using an interactive website -- gpucomputing.net. This network will serve a broad array of user-driven communities across the wide range of areas in which GPU computing is beneficial: from medical imaging and financial modeling to fluid dynamics and video processing. The need for such networked communities arises from the dramatic growth of GPU computing worldwide. GPU computing refers to the use of GPUs (graphics processing units) for scientific and engineering applications and research in a heterogeneous computing environment.

The website will be structured by application domains, enabling users to access colleagues, findings, data, forums, and news items in their own fields of research, with ready access to other communities and application domains. Community resources, on the other hand, will be completely integrated, thus encouraging interdisciplinary collaborations.

The site is open to all for top-level viewing, but membership is required for participation in the communities. Students and researchers with “.edu” email addresses are automatically accepted as members, while others can request membership simply by being referred by an existing member.

Participants in the CUDA Centers of Excellence (CCOEs), sponsored by NVIDIA, will be active in the network, in recognition of their institutions’ work at the forefront of massively parallel computing research. CUDA Centers of Excellence include University of Illinois at Urbana-Champaign, University of Tennessee, Harvard University, and University of Utah in the US; Cambridge University in Europe; Institute of Process Engineering (IPE) at the Chinese Academy of Sciences (CAS) and Tsinghua University in China; and National Taiwan University in Taiwan.