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Sparse matrix solvers on the GPU: conjugate gradients and multigrid
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 . 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.
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Cited by
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Grady, L. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.233 · Cited by 2,624
Pérez, P., Gangnet, M., & Blake, A. (2003) · ACM Transactions on Graphics · doi:10.1145/882262.882269 · Cited by 1,773
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Steinkraus, D., Buck, I., & Simard, P. Y. (2005) · Eighth International Conference on Document Analysis and Recognition (ICDAR'05) · doi:10.1109/icdar.2005.251 · Cited by 251
Grady, L., Schiwietz, T., Aharon, S., & Westermann, R. (2005) · Lecture Notes in Computer Science · doi:10.1007/11566489_95 · Cited by 113
Metadata
- Authors
- Bolz, J., Farmer, I., Grinspun, E., & Schröder, P.
- Venue
- ACM Transactions on Graphics
- Published
- 2003-07
- Volume
- 22
- Issue
- 3
- Pages
- 917-924
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2997945685
- Cited by
- 540
- References
- 0
- FWCI
- 44.63
- Type
- journal article
- Language
- en
- Open access
- closed
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- Bolz, J., Farmer, I., Grinspun, E., & Schröder, P. (2003). Sparse matrix solvers on the GPU: conjugate gradients and multigrid. ACM Transactions on Graphics. https://doi.org/10.1145/882262.882364
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