Type of Document Master's Thesis Author Eller, Paul Ray Author's Email Address firstname.lastname@example.org URN etd-07172009-171608 Title Development and Acceleration of Parallel Chemical Transport Models Degree Master of Science Department Computer Science Advisory Committee
Advisor Name Title Sandu, Adrian Committee Chair Nikolopoulos, Dimitrios S. Committee Member Ribbens, Calvin J. Committee Member Keywords
Date of Defense 2009-07-14 Availability unrestricted AbstractImproving chemical transport models for atmospheric simulations relies on future developments of mathematical methods and parallelization methods. Better mathematical methods allow simulations to more accurately model realistic processes and/or to run in a shorter amount of time. Parellization methods allow simulations to run in much shorter amounts of time, therefore allowing scientists to use more accurate or more detailed simulations (higher resolution grids, smaller time steps).
The state-of-the-science GEOS-Chem model is modified to use the Kinetic Pre-Processor, giving users access to an array of highly efficient numerical integration methods and to a wide variety of user options. Perl parsers are developed to interface GEOS-Chem with KPP in addition to modifications to KPP allowing KPP integrators to interface with GEOS-Chem. A variety of different numerical integrators are tested on GEOS-Chem, demonstrating that KPP provided chemical integrators produce more accurate solutions in a given amount of time than the original GEOS-Chem chemical integrator.
The STEM chemical transport model provides a large scale end-to-end application to experiment with running chemical integration methods and transport methods on GPUs. GPUs provide high computational power at a fairly cheap cost. The CUDA programming environment simplifies the GPU development process by providing access to powerful functions to execute parallel code. This work demonstrates the accleration of a large scale end-to-end application on GPUs showing significant speedups. This is achieved by implementing all relevant kernels on the GPU using CUDA. Nevertheless, further improvements to GPUs are needed to allow these applications to fully exploit the power of GPUs.
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