INDUSTRY
GridChem Enhances Client With Web Services
The Computational Chemistry Grid (CCG) released its new GridChem client. The beta release of the client has been available since mid-November. The CCG client, GridChem, is a Java desktop application that runs on any platform as a chemistry workbench and interfaces to high performance computing (HPC) systems for quantum application execution. The GridChem cyberinfrastructure integrates remote HPC systems, applications, the desktop client, and middleware grid technologies to create an intuitive, easy-to-use system for solving quantum chemistry problems at its Web site.
“We are proud of what our chemistry cyberinfrastructure can do for the scientific community,” said Dr. John Connolly, GridChem principal investigator and director of the University of Kentucky Center for Computational Sciences. “The developers have accomplished a great deal since the project began in 2004. We listened to our users and the resulting client – along with the architectural changes that will accelerate adaptation of new services – will be of great benefit to the community,” said Connolly.
Some features in the new client include:
- Secure, session-based interaction with the middleware
- Real-time accounting and usage information
- Automatic job status updates
- New, feature-rich resource and queue monitoring interface
- Redesigned file browser
- Native text editing of output files
- Full help documentation integrated within the client
- Resource status notifications (e.g., scheduled maintenance) delivered to client
For more information about the project and to download the new client, visit GridChem at its Web site.
GridChem partners include the Center for Computational Sciences / University of Kentucky, Center for Computation and Technology / Louisiana State University, National Center for Supercomputing Applications (NCSA) / University of Illinois
Urbana-Champaign, Ohio Supercomputer Center / The Ohio State University, and Texas Advanced Computing Center (TACC) / The University of Texas at Austin. The project is supported by the National Science Foundation NMI Program under Award #04-38312.