A framework for exploring numerical solutions of advection-reaction-diffusion equations using a GPU-based approach

Allen Sanderson, Miriah Meyer, Robert Kirby, Chris Johnson
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA
Computing and Visualization in Science, Vol. 12, No. 4. (1 April 2009), pp. 155-170.


   title={A framework for exploring numerical solutions of advection–reaction–diffusion equations using a GPU-based approach},

   author={Sanderson, A.R. and Meyer, M.D. and Kirby, R.M. and Johnson, C.R.},

   journal={Computing and visualization in science},








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In this paper we describe a general purpose, graphics processing unit (GP-GPU)-based approach for solving partial differential equations (PDEs) within advection-reaction-diffusion models. The GP-GPU-based approach provides a platform for solving PDEs in parallel and can thus significantly reduce solution times over traditional CPU implementations. This allows for a more efficient exploration of various advection-reaction-diffusion models, as well as, the parameters that govern them. Although the GPU does impose limitations on the size and accuracy of computations, the PDEs describing the advection-reaction-diffusion models of interest to us fit comfortably within these constraints. Furthermore, the GPU technology continues to rapidly increase in speed, memory, and precision, thus applying these techniques to larger systems should be possible in the future. We chose to solve the PDEs using two numerical approaches: for the diffusion, a first-order explicit forward Euler solution and a semi-implicit second order Crank-Nicholson solution; and, for the advection and reaction, a first-order explicit solution. The goal of this work is to provide motivation and guidance to the application scientist interested in exploring the use of the GP-GPU computational framework in the course of their research. In this paper, we present a rigorous comparison of our GPU-based advection-reaction-diffusion code model with a CPU-based analog, finding that the GPU model out-performs the CPU implementation in one-to-one comparisons.
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