Optimization principles and application performance evaluation of a multithreaded GPU using CUDA
Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming
Scalable Parallel Programming with CUDA
Queue - GPU Computing
A Fast Iterative Method for Eikonal Equations
SIAM Journal on Scientific Computing
Journal of Parallel and Distributed Computing
Journal of Computational Physics
GPU parallelization of a three dimensional marine CSEM code
Computers & Geosciences
Massively parallel regularized 3D inversion of potential fields on CPUs and GPUs
Computers & Geosciences
Population-based harmony search using GPU applied to protein structure prediction
International Journal of Computational Science and Engineering
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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.