Sparse matrix solvers on the GPU: conjugate gradients and multigrid
Sparse matrix solvers on the GPU: conjugate gradients and multigrid
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DOI：
10.1145/1198555.1198781
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发表时间：
2003-07
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影响因子：
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通讯作者：
J. Bolz;I. Farmer;E. Grinspun;P. Schröder
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文献类型：
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作者：
J. Bolz;I. Farmer;E. Grinspun;P. Schröder
许多计算机图形应用需要高强度的数值模拟。我们表明，这样的计算可以有效地在GPU上进行，我们认为这是一个具有高浮点性能的全功能流处理器。我们实现了两个基本的，广泛有用的，计算内核：稀疏矩阵共轭梯度求解器和规则网格多重网格求解器。从网格平滑和参数化到流体求解器和固体力学的实时应用程序都可以从这些应用程序中受益匪浅，这证明了我们在NVIDIA GeForce FX上运行的几何流和流体模拟应用程序的示例。
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.