Systems Status

8/3/06

The /quicksand filesystem is again available. Please move your large data sets and run your jobs out of /quicksand.

7/28/06

We recently installed a new Debian-based operating system on Hemisphere. If your favorite software is missing, please contact the administration team. Note that the SSH key changed, so you may need to edit your ~/.ssh/known_hosts file to connect.

7/29/05

The Computational Science Center is seeking qualified graduate or undergraduate students to join our research team. For more information, please see Jobs with CSC.

6/27/05

To receive important announcements regarding CSC systems, such as upcoming downtime, please subscribe to the csc-announce mailing list.

The Computational Science Center (CSC) is a multidisciplinary research group with research initiatives focusing on high-performance, parallel, cluster, grid and cloud computing systems, emerging parallel architectures, and scientific application development. In addition, the CSC manages the University of Colorado’s Department of Computer Science’s supercomputing facility.

If you are new to the CSC, the links below will help you with operating the systems and software provided by the center. If you have any questions, feel free to email rt@csc.cs.colorado.edu.

Additional documentation for other systems can be found in the Systems section.

Research Spotlight

> HOMME on the IBM BlueGene/L

NCAR researchers, funded in part by the Department of Energy’s Climate Change Prediction Program, have built a scalable and efficient spectral-element-based atmospheric dynamical core using the Computational Science Section’s High Order Method Modeling Environment (HOMME). In order for this to be a useful tool for atmospheric scientists it is necessary to couple this core to physics packages employed by the community.

The physics of cloud formation is generally simulated rather crudely using phenomenological parameterizations. The dream of modelers is the direct numerical simulation of cloud processes on a global scale. Unfortunately, this requires an increase in computational power of approximately six orders of magnitude over what is currently available. A promising alternative to improve the simulation of cloud processes in climate models is a compromise technique called Cloud Resolving Convective Parameterization (CRCP, also known as Super-Parameterization). The cost of this approach is two to three orders of magnitude more expensive than traditional parameteriz ation techniques. However, with the advent of BlueGene/L this is now tractable. We have built a super-parameterization package and work is underway to couple this to HOMME. The result will be an atmospheric model capable of exploiting BG/L’s scalability and computational power to realize practical and scientifically useful integration rates for super-parameterized climate simulation.