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.
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.