Improved Quasi-Steady-State-Approximation Methods for Atmospheric Chemistry Integration
SIAM Journal on Scientific Computing
Exponential Integrators for Large Systems of Differential Equations
SIAM Journal on Scientific Computing
Predicting air quality: Improvements through advanced methods to integrate models and measurements
Journal of Computational Physics
Optimizing large scale chemical transport models for multicore platforms
Proceedings of the 2008 Spring simulation multiconference
Large calculation of the flow over a hypersonic vehicle using a GPU
Journal of Computational Physics
Vector stream processing for effective application of heterogeneous parallelism
Proceedings of the 2009 ACM symposium on Applied Computing
Solving the euler equations on graphics processing units
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
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Improving 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. Parallelization methods allow simulations to run in less time, allowing scientists to use more accurate or more detailed simulations (higher resolution grids, smaller time steps). 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 acceleration 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.