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Contribution to Book
High Rayleigh Number Mantle Convection on GPU
GPU Solutions to Multi-scale Problems in Science and Engineering (2013)
  • David A. Sanchez, University of Minnesota
  • Christopher Gonzalez, University of Minnesota
  • David A. Yuen, University of Minnesota
  • Grady B. Wright, Boise State University
  • Gregory A. Barnett, University of Colorado Boulder
We implemented two- and three-dimensional Rayleigh-Benard convection on Nvidia GPUs by utilizing a 2nd-order finite difference method. By exploiting the massive parallelism of GPU using both CUDA for C and optimized CUBLAS routines, we have on a single Fermi GPU run simulations of Rayleigh number up to 6×1010 (on a mesh of 2000×4000 uniform grid points) in two dimensions and up to 107 (on a mesh of 450×450×225 uniform grid points) for three dimensions. On Nvidia Tesla C2070 GPUs, these implementations enjoy single-precision performance of 535 GFLOP/s and 100 GFLOP/s respectively, and double-precision performance of 230 GFLOP/s and 70 GFLOP/s respectively.
Publication Date
David A. Yuen, Long Wang, Xuebin Chi, Lennart Johnsson, Wei Ge, Yaolin Shi
Lecture Notes in Earth Systems Sciences
Citation Information
David A. Sanchez, Christopher Gonzalez, David A. Yuen, Grady B. Wright, et al.. "High Rayleigh Number Mantle Convection on GPU" HeidelbergGPU Solutions to Multi-scale Problems in Science and Engineering (2013)
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