Massively parallel forward modeling of scalar and tensor gravimetry data
We present an approach to calculate scalar and tensor gravity utilizing the massively parallel architecture of consumer graphics cards. Our parametrization is based on rectilinear blocks with constant density within each blocks. This type of parametrization is well suited for inversion of gravity data or joint inversion with other datasets, but requires the calculation of a large number of model blocks for complex geometries. For models exceeding 10,000 cells we achieve an acceleration of a factor of 40 for scalar data and 30 for tensor data compared to a single thread on the CPU. This significant acceleration allows fast computation of large models exceeding 10(6) model parameters and thousands of measurement sites. (C) 2010 Elsevier Ltd. All rights reserved.
gravity modeling cuda parallel computing