Sparse Matrix Solvers on the GPU: Conjugate Gradients and Multigrid
Jeff Bolz, Ian Farmer, Eitan Grinspun, Peter Schröder
In ACM Transactions on Graphics, 22(3), July 2003.
Abstract: Many computer graphics applications require high-intensity numerical simulation. We show that such computations can be performed efficiently on the GPU, which we regard as a full function streaming processor with high floating-point performance. We implemented two basic, broadly useful, computational kernels: a sparse matrix conjugate gradient solver and a regular-grid multigrid solver. Realtime applications ranging from mesh smoothing and parameterization to fluid solvers and solid mechanics can greatly benefit from these, evidence our example applications of geometric flow and fluid simulation running on NVIDIA's GeForce FX.
Keyword(s): GPU Computing, Numerical Simulation, Conjugate Gradient, Multigrid,Mesh Smoothing, Fluid Simulation, Navier-Stokes
BibTeX format:
@article{Bolz:2003:SMS,
  author = {Jeff Bolz and Ian Farmer and Eitan Grinspun and Peter Schröder},
  title = {Sparse Matrix Solvers on the GPU: Conjugate Gradients and Multigrid},
  journal = {ACM Transactions on Graphics},
  volume = {22},
  number = {3},
  pages = {917--924},
  month = jul,
  year = {2003},
}
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