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Sparse Matrix Solvers on the GPU: Conjugate Gradients and Multigrid

Bolz, Jeff and Farmer, Ian and Grinspun, Eitan and Schröder, Peter (2003) Sparse Matrix Solvers on the GPU: Conjugate Gradients and Multigrid. In: SIGGRAPH '03 ACM SIGGRAPH 2003 Papers. ACM , New York, NY, pp. 917-924. ISBN 1-58113-709-5.

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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. Real-time 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.

Item Type:Book Section
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Schröder, Peter0000-0002-0323-7674
Additional Information:© 2003 ACM. This work was supported in part by NSF (DMS-0220905, DMS-0138458, ACI-0219979), the DOE (W-7405-ENG-48/B341492), NVIDIA, the Center for Integrated Multiscale Modeling and Simulation, Alias|Wavefront, Pixar, and the Packard Foundation. Special thanks to Matt Papakipos, Nick Triantos, David Kirk, Paul Keller, Mark Meyer, Mika Nyström, Niles Pierce, Burak Aksoylu, Michael Holst, Jason Hickey, André DeHon, Ian Buck, Mark Harris and all the speakers and students in the “Hacking the GPU” class (Caltech, Fall 2002).
Funding AgencyGrant Number
Department of Energy (DOE)W-7405-ENG-48/B341492
Center for Integrated Multiscale Modeling and SimulationUNSPECIFIED
David and Lucile Packard FoundationUNSPECIFIED
Subject Keywords:GPU Computing, Numerical Simulation, Conjugate Gradient, Multigrid, Mesh Smoothing, Fluid Simulation, Navier-Stokes
Classification Code:CR Categories: I.3.1 [Computer Graphics]: Hardware Architecture—Graphics pro- cessors; G.1.3 [Numerical Analysis]: Numerical Linear Algebra—Sparse, structured, and very large systems (direct and iterative methods); G.1.8 [Numerical Analysis]: Partial Diff
Record Number:CaltechAUTHORS:20160809-162351341
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:69522
Deposited On:10 Aug 2016 17:48
Last Modified:09 Feb 2023 23:30

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