Ternary Sparse Matrix Representation for Volumetric Mesh Subdivision and Processing on GPUs.
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| Title: | Ternary Sparse Matrix Representation for Volumetric Mesh Subdivision and Processing on GPUs. |
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| Authors: | Mueller‐Roemer, J. S.1, Altenhofen, C.1, Stork, A.1 |
| Source: | Computer Graphics Forum. Aug2017, Vol. 36 Issue 5, p59-69. 11p. |
| Subjects: | Sparse matrix software, Volumetric analysis, Graphics processing units, Mesh networks, Ternary system |
| Abstract: | In this paper, we present a novel volumetric mesh representation suited for parallel computing on modern GPU architectures. The data structure is based on a compact, ternary sparse matrix storage of boundary operators. Boundary operators correspond to the first-order top-down relations of k-faces to their (k − 1)-face facets. The compact, ternary matrix storage format is based on compressed sparse row matrices with signed indices and allows for efficient parallel computation of indirect and bottom-up relations. This representation is then used in the implementation of several parallel volumetric mesh algorithms including Laplacian smoothing and volumetric Catmull-Clark subdivision. We compare these algorithms with their counterparts based on OpenVolumeMesh and achieve speedups from 3× to 531×, for sufficiently large meshes, while reducing memory consumption by up to 36%. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | In this paper, we present a novel volumetric mesh representation suited for parallel computing on modern GPU architectures. The data structure is based on a compact, ternary sparse matrix storage of boundary operators. Boundary operators correspond to the first-order top-down relations of k-faces to their (k − 1)-face facets. The compact, ternary matrix storage format is based on compressed sparse row matrices with signed indices and allows for efficient parallel computation of indirect and bottom-up relations. This representation is then used in the implementation of several parallel volumetric mesh algorithms including Laplacian smoothing and volumetric Catmull-Clark subdivision. We compare these algorithms with their counterparts based on OpenVolumeMesh and achieve speedups from 3× to 531×, for sufficiently large meshes, while reducing memory consumption by up to 36%. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 01677055 |
| DOI: | 10.1111/cgf.13245 |
