A low-cost-memory CUDA implementation of the conjugate gradient method applied to globally supported radial basis functions implicits.
Journal of Computational Science(2014)
摘要
Hermitian radial basis functions implicits is a method capable of reconstructing implicit surfaces from first-order Hermitian data. When globally supported radial functions are used, a dense symmetric linear system must be solved. In this work, we aim at exploring and computing a matrix-free implementation of the Conjugate Gradients Method on the GPO in order to solve such linear system. The proposed method parallelly rebuilds the matrix on demand for each iteration. As a result, it is able to compute the Hermitian-based interpolant for datasets that otherwise could not be handled due to the high memory demanded by their linear systems. (C) 2014 Elsevier B.V. All rights reserved.
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关键词
Radial basis function interpolation,CUDA,Conjugate Gradients Method
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