Speaker: David A. Yuen
The faster growth curves in the speed of GPUs relative to CPUs in recent years and its rapidly gained popularity have spawned a new area of development in computational technology. There is much potential in utilizing GPUs for solving evolutionary partial differential equations . We are concerned with modeling tsunami waves, where computational time is of extreme importance. We have employed the NVIDIA board 8600M GT on a MacPro to test the efficacy of the GPU on the set of shallow-water equations. We have compared the relative speeds between CPU and GPU on a single processor for two types of spatial discretization based on second-order finite-differences and radial basis functions, which is a more novel method based on a gridless and a multi-scale, adaptive framework. For the NVIDIA 8600M GT we found a speed up by a factor of 8 in favor of GPU for the finite-difference method and a factor of 7 for the RBF scheme. WThe timesteps employed for the RBF method are larger than those used in finite-differences, because of the much fewer number of nodal points needed by RBF. This ratio, favoring the RBFs over the finite-difference points, increases with the number of grid points. Thus, in modeling wave-propagation over the same physical time transpired , RBF acting in concert with GPU would be the fastest way to go.<\/p>\n","protected":false},"excerpt":{"rendered":"