HR: 1340h
AN: IN43B-1182    [Abstracts]
TI: Enabling GPU Acceleration of HIRDLS Scientific Data Processing
AU: Craft, J V
EM: jcraft@ucar.edu
AF: University of Colorado Center for Limb Atmospheric Sounding, 3450 Mitchell Lane Building FL-0, Boulder, CO 80301, United States
AU: * Ellis, D T
EM: dtellis@ucar.edu
AF: University of Colorado Center for Limb Atmospheric Sounding, 3450 Mitchell Lane Building FL-0, Boulder, CO 80301, United States
AU: Cavanaugh, C
EM: cavanaug@ucar.edu
AF: National Center for Atmospheric Research, 3450 Mitchell Lane Building FL-0, Boulder, CO 80301, United States
AU: Gille, J G
EM: gille@ucar.edu
AF: University of Colorado Center for Limb Atmospheric Sounding, 3450 Mitchell Lane Building FL-0, Boulder, CO 80301, United States
AU: Barnett, J J
EM: j.barnett1@physics.ox.ac.uk
AF: Oxford University Department of Atmospheric Physics, Clarendon Lab Parks Road, Oxford, OX1 3PU, United Kingdom
AB: The HIRDLS team involves scientists, software developers and students from Atmospheric Science, Computer Science and various engineering disciplines, and from numerous agencies, universities, and centers. The focus of research is determining vertical profiles of the Earth's atmospheric chemical composition from infrared samplings. The processing techniques used to calculate the scientific data rely heavily on mathematical algorithms. While the data processing involves large sets of data, there is much repetition of smaller well defined kernels. Currently all processing has been done on large, expensive, CPU clusters. Programming these machines effectively to take advantage of obvious parallelism in the data streams is difficult and time consuming. In this paper we will show the suitability for scientific processing in a GPGPU environment. Given the nature of the data streams as well as the types of processing, it will be shown that there can be a very high potential benefit in using GPUs. We will show several examples of how GPGPU will directly impact the current production processing, as well as some other off-line housekeeping procedures. Also, we will demonstrate the usefulness of GPGPU programs to accelerate in the field calculations performed by scientists in various programming environments. Lastly, we will present a timeline for enabling GPGPU in the HIRDLS processing environment and the expected impact on future processing of science data.
DE: 3300 ATMOSPHERIC PROCESSES
DE: 3360 Remote sensing
SC: Earth and Space Science Informatics [IN]
MN: 2007 Fall Meeting