High-performance land surface modeling with a Linux cluster

  • Authors:

  • Y. Tian;C. D. Peters-Lidard;S. V. Kumar;J. Geiger;P. R. Houser;J. L. Eastman;P. Dirmeyer;B. Doty;J. Adams

  • Affiliations:

  • University of Maryland at Baltimore County, Goddard Earth Sciences Technology Center, Baltimore, MD 21250, USA and NASA, Goddard Space Flight Center, Hydrological Sciences Branch, Mail Code 614.3, ...;NASA, Goddard Space Flight Center, Hydrological Sciences Branch, Mail Code 614.3, Greenbelt, MD 20771, USA;University of Maryland at Baltimore County, Goddard Earth Sciences Technology Center, Baltimore, MD 21250, USA;NASA, Goddard Space Flight Center, Information Systems Division, Code 580, Greenbelt, MD 20771, USA;George Mason University, Climate Dynamics Program and Center for Research in Environment and Water, Calverton, MD 20705, USA;University of Maryland at Baltimore County, Goddard Earth Sciences Technology Center, Baltimore, MD 21250, USA;Center for Ocean-Land-Atmosphere Studies, Calverton, MD 20705, USA;Center for Ocean-Land-Atmosphere Studies, Calverton, MD 20705, USA;Center for Ocean-Land-Atmosphere Studies, Calverton, MD 20705, USA

  • Venue:
  • Computers & Geosciences
  • Year:
  • 2008

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Abstract

The Land Information System (LIS) was developed at NASA to perform global land surface simulations at a resolution of 1-km or finer in real time. Such unprecedented scales and intensity pose many computational challenges. In this article, we demonstrate some of our approaches in high-performance computing with a Linux cluster to meet these challenges and reach our performance goals. These approaches include job partition and a job management system for parallel processing on the cluster, high-performance parallel input/output based on GrADS-DODS (GDS) servers, dynamic load-balancing and distributed data storage techniques, and highly scalable data replication with peer-to-peer (P2P) technology. These techniques work coherently to provide a high-performance land surface modeling system featuring fault tolerance, optimal resource utilization, and high scalability. Examples are given with LIS's high-resolution modeling of surface runoff during 2003 to illustrate LIS's capability to enable new scientific explorations.