Dissertation/Thesis Abstract

Parallelizing a data intensive Lagrangian stochastic particle model using graphics processing units
by Hurst, Jonathan George, M.S., University of Colorado at Boulder, 2010, 101; 1481219
Abstract (Summary)

Atmospheric transport and dispersion (T&D) models play an important roll in United States national defense. Due to operational time constraints, less sophisticated models have consistently dominated the defense market. Recent advances in graphics processing units (GPUs) and their programming models have made GPUs an attractive platform for commodity, low-power, high-performance parallel computing. Two GPU accelerated (using NVIDIA Corporation's CUDA technology) versions of a sophisticated, large-eddy simulation (LES) based, Lagrangian stochastic model, developed at the National Center for Atmospheric Research (NCAR), were implemented and compared against their single and multiple core CPU (Intel Harpertown) counterparts. The implementation representing the shortest route to GPU acceleration observed a single GPU speedup of 14x over the single core CPU implementation. A more robust and scalable single GPU implementation observed speedups of 20x over the single core CPU implementation.

Indexing (document details)
Advisor: Tufo, Henry
Commitee: Siek, Jeremy G., Vachharajani, Manish
School: University of Colorado at Boulder
Department: Computer Science
School Location: United States -- Colorado
Source: MAI 49/01M, Masters Abstracts International
Subjects: Atmospheric sciences, Computer science
Keywords: Dispersion, Gpu, Graphics, Lagrangian, Nvidia, Transport
Publication Number: 1481219
ISBN: 978-1-124-22375-9
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