2007年清华大学信息技术研究院系列学术报告11

Prof. Geoffrey Charles Fox received a Ph.D. in Theoretical Physics from Cambridge University and is now professor of Computer Science, Informatics, and Physics at Indiana University. He is director of the Community Grids Laboratory of the Pervasive Technology Laboratories at Indiana University. He previously held positions at Caltech, Syracuse University and Florida State University. He has published over 550 papers in physics and computer science and been a major author on four books. Fox has worked in a variety of applied computer science fields with his work on computational physics evolving into contributions to parallel computing and now to Grid and multicore chip systems. His interest in education includes Internet delivery of courses and development of new curricula for interdisciplinary studies. He has worked on the computing issues in several application areas – currently focusing on Defense, Earthquake and Ice-sheet Science and Chemical Informatics. Parallel computing on multicore chips is a major research focus. He is currently Vice President of the Open Grid Forum responsible for eScience. He is involved in several projects to enhance the capabilities of Minority Serving Institutions including three funded by NSF CI-Team. His role in these is linking MSI’s to the international Grid and computational science communities.

Abstract:

We discuss the application of Web 2.0 to support scientific research (e-Science) and related e-more or less anything applications. Web 2.0 offers interesting technical approaches to build the core e-infrastructure (Cyberinfrastructure) as well as a host of interesting services exemplified by Facebook, YouTube, Amazon S3/EC2 and Google maps. We discuss why some of the original Grid goals of linking the world's computer systems may not be so relevant today and that interoperability is needed at the data and not always at the infrastructure level. Web 2.0 may also support Parallel Programming 2.0 -- a better parallel computing software environment motivated by the need to run commodity applications on multicore chips.