Paper No. 6
Presentation Time: 2:15 PM


FRAME, Michael T., U.S Geological Survey, Core Science Analytics & Synthesis, 230 Warehouse Road, Oak Ridge, TN 37831, FALGOUT, Jeff, U.S Geological Survey, Core Science Analytics & Synthesis, West 6th Ave. & Kipling St, DFC, Denver, CO 80225 and PALANISAMY, Giri, Environmental Sciences, ORNL, Oak Ridge, TN 37831,

Addressing grand environmental science challenges requires unprecedented access to easily understood data that cross the breadth of temporal, spatial, and thematic scales. From a scientist’s perspective, the big challenges lie in discovering the relevant data, dealing with extreme data heterogeneity, large data volumes, and converting data to information and knowledge. Improved high performance computing methods, capabilities, and access allows USGS scientists to perform these complex analyses in significantly less time and with greater breath/scale. The USGS Core Science Analytics and Synthesis (CSAS) organization is leading this research effort to investigate the most effective and sustainable approaches to support these data-intensive science research, analysis, and management requirements.

Through this effort, it is anticipated that USGS scientists will gain access to internal and partner organizations’ high performance computing resources and thereby helping to eliminate computational barriers to science within the USGS. The effort aims to provide value to science through expertise in research computing; increasing accessibility to high performance computing resources (such as one of the worlds fastest computer, Titan, housed at the U.S Department of Energy’s Oak Ridge National Laboratory); offering educational and user support services; growth of partnerships and community collaborations; and leadership and advocacy within the USGS for sustainability of computational science capabilities.

The session will discuss the current pilot project undertaken, status of educational/outreach activities, and the future evolution of this USGS data intensive scientific computing effort.

  • GSA HPC - Frame.pptx (9.4 MB)