On the Convergence of the Jacobi Method for Arbitrary Orderings
SIAM Journal on Matrix Analysis and Applications
A parallel ring ordering algorithm for efficient one-sided Jacobi SVD computations
Journal of Parallel and Distributed Computing
A user-programmable vertex engine
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Ray tracing on programmable graphics hardware
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
An Eulerian description of the streaming process in the lattice Boltzmann equation
Journal of Computational Physics
Nonlinear optimization framework for image-based modeling on programmable graphics hardware
ACM SIGGRAPH 2003 Papers
Brook for GPUs: stream computing on graphics hardware
ACM SIGGRAPH 2004 Papers
Accelerating the SVD Block-Jacobi Method
Computing - Editorial: Special issue on GAMM – Workshop on Guaranteed Error-bounds for the Solution of Nonlinear Problems in Applied Mathematics
New Fast and Accurate Jacobi SVD Algorithm. I
SIAM Journal on Matrix Analysis and Applications
New Fast and Accurate Jacobi SVD Algorithm. II
SIAM Journal on Matrix Analysis and Applications
TeraFLOP computing on a desktop PC with GPUs for 3D CFD
International Journal of Computational Fluid Dynamics - Mesoscopic Methods And Their Applications To CFD
Journal of Parallel and Distributed Computing
Accelerating Lattice Boltzmann Fluid Flow Simulations Using Graphics Processors
ICPP '09 Proceedings of the 2009 International Conference on Parallel Processing
Concurrency and Computation: Practice & Experience - Proceedings of the 6th ACES Symposium, May 11–16, 2008, Cairns, Australia
CPU/GPU computing for long-wave radiation physics on large GPU clusters
Computers & Geosciences
Accelerating batch processing of spatial raster analysis using GPU
Computers & Geosciences
GPU-based roofs' solar potential estimation using LiDAR data
Computers & Geosciences
Comparing the performance of stochastic simulation on GPUs and OpenMP
International Journal of Computational Science and Engineering
Use of GPU computing for uncertainty quantification in computational mechanics: A case study
Scientific Programming
Hi-index | 0.00 |
Many complex natural systems studied in the geosciences are characterized by simple local-scale interactions that result in complex emergent behavior. Simulations of these systems, often implemented in parallel using standard central processing unit (CPU) clusters, may be better suited to parallel processing environments with large numbers of simple processors. Such an environment is found in graphics processing units (GPUs) on graphics cards. This paper discusses GPU implementations of three example applications from computational fluid dynamics, seismic wave propagation, and rock magnetism. These candidate applications involve important numerical modeling techniques, widely employed in physical system simulations, that are themselves examples of distinct computing classes identified as fundamental to scientific and engineering computing. The presented numerical methods (and respective computing classes they belong to) are: (1) a lattice-Boltzmann code for geofluid dynamics (structured grid class); (2) a spectral-finite-element code for seismic wave propagation simulations (sparse linear algebra class); and (3) a least-squares minimization code for interpreting magnetic force microscopy data (dense linear algebra class). Significant performance increases (between 10x and 30x in most cases) are seen in all three applications, demonstrating the power of GPU implementations for these types of simulations and, more generally, their associated computing classes.