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- W2093184413 abstract "Abstract 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 GPU 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." @default.
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- W2093184413 date "2014-09-01" @default.
- W2093184413 modified "2023-09-23" @default.
- W2093184413 title "A low-cost-memory CUDA implementation of the conjugate gradient method applied to globally supported radial basis functions implicits" @default.
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- W2093184413 doi "https://doi.org/10.1016/j.jocs.2014.06.007" @default.
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