#### Event Title

Accelerating the Computation and Verification of Molecular Collision Models

#### Presentation Type

Oral and/or Visual Presentation

#### Presenter Major(s)

Computer Science, Mathematics

#### Mentor Information

Christian Trefftz, trefftzc@gvsu.edu; Greg Wolffe, wolffe@gvsu.edu

#### Department

School of Computing and Information Systems

#### Location

Kirkhof Center 2216

#### Start Date

13-4-2011 10:00 AM

#### End Date

13-4-2011 10:30 AM

#### Keywords

Mathematical Science, Physical Science, Technology

#### Abstract

Our project constituted a case study in computational science: applying parallel computing techniques to mathematical models for solving a scientific problem. The problem involved a physical chemistry model that evaluated simulations of molecular collision experiments. The collision model was implemented via a 15,000-line FORTRAN-77 simulation. This project was chosen for parallelization because of its extreme computational complexity and significant execution time. We targeted two new and different technologies to parallelize the simulation: OpenMP and CUDA FORTRAN. Nearly linear speedup was measured in the OpenMP parallel version executing on a 16-core multiprocessor. Experimental data indicates speedups should continue to scale well with an increasing number of processors. Results from the CUDA FORTRAN parallel version executing on a graphical processing unit are still pending, but we predict greater speedups will be observed since modern GPUs contain hundreds of stream processors.

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Accelerating the Computation and Verification of Molecular Collision Models

Kirkhof Center 2216

Our project constituted a case study in computational science: applying parallel computing techniques to mathematical models for solving a scientific problem. The problem involved a physical chemistry model that evaluated simulations of molecular collision experiments. The collision model was implemented via a 15,000-line FORTRAN-77 simulation. This project was chosen for parallelization because of its extreme computational complexity and significant execution time. We targeted two new and different technologies to parallelize the simulation: OpenMP and CUDA FORTRAN. Nearly linear speedup was measured in the OpenMP parallel version executing on a 16-core multiprocessor. Experimental data indicates speedups should continue to scale well with an increasing number of processors. Results from the CUDA FORTRAN parallel version executing on a graphical processing unit are still pending, but we predict greater speedups will be observed since modern GPUs contain hundreds of stream processors.