Title page for ETD etd-05082010-155149

Type of Document Dissertation
Author Linford, John Christian
Author's Email Address jlinford@vt.edu
URN etd-05082010-155149
Title Accelerating Atmospheric Modeling Through Emerging Multi-core Technologies
Degree PhD
Department Computer Science
Advisory Committee
Advisor Name Title
Sandu, Adrian Committee Chair
Marr, Linsey C. Committee Member
Nikolopoulos, Dimitrios S. Committee Member
Plassmann, Paul E. Committee Member
Ribbens, Calvin J. Committee Member
  • hardware
  • high performance computing
  • software
Date of Defense 2010-05-05
Availability unrestricted
The new generations of multi-core chipset architectures achieve unprecedented levels of computational power while respecting physical and economical constraints. The cost of this power is bewildering program complexity. Atmospheric modeling is a grand-challenge problem that could make good use of these architectures if they were more accessible to the average programmer. To that end, software tools and programming methodologies that greatly simplify the acceleration of atmospheric modeling and simulation with emerging multi-core technologies are developed. A general model is developed to simulate atmospheric chemical transport and atmospheric chemical kinetics. The Cell Broadband Engine Architecture (CBEA), General Purpose Graphics Processing Units (GPGPUs), and homogeneous multi-core processors (e.g. Intel Quad-core Xeon) are introduced. These architectures are used in case studies of transport modeling and kinetics modeling and demonstrate per-kernel speedups as high as 40x. A general analysis and code generation tool for chemical kinetics called "KPPA" is developed. KPPA generates highly tuned C, Fortran, or Matlab code that uses every layer of heterogeneous parallelism in the CBEA, GPGPU, and homogeneous multi-core architectures. A scalable method for simulating chemical transport is also developed. The Weather Research and Forecasting Model with Chemistry (WRF-Chem) is accelerated with these methods with good results: real forecasts of air quality are generated for the Eastern United States 65% faster than the state-of-the-art models.
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