EGGS: Sparsity-Specific Code Generation

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dc.contributor.authorTang, Xuanen_US
dc.contributor.authorSchneider, Teseoen_US
dc.contributor.authorKamil, Shoaiben_US
dc.contributor.authorPanda, Aurojiten_US
dc.contributor.authorLi, Jinyangen_US
dc.contributor.authorPanozzo, Danieleen_US
dc.contributor.editorJacobson, Alec and Huang, Qixingen_US
dc.date.accessioned2020-07-05T13:26:16Z
dc.date.available2020-07-05T13:26:16Z
dc.date.issued2020
dc.description.abstractSparse matrix computations are among the most important computational patterns, commonly used in geometry processing, physical simulation, graph algorithms, and other situations where sparse data arises. In many cases, the structure of a sparse matrix is known a priori, but the values may change or depend on inputs to the algorithm. We propose a new methodology for compile-time specialization of algorithms relying on mixing sparse and dense linear algebra operations, using an extension to the widely-used open source Eigen package. In contrast to library approaches optimizing individual building blocks of a computation (such as sparse matrix product), we generate reusable sparsity-specific implementations for a given algorithm, utilizing vector intrinsics and reducing unnecessary scanning through matrix structures. We demonstrate the effectiveness of our technique on a benchmark of artificial expressions to quantitatively evaluate the benefit of our approach over the state-ofthe- art library Intel MKL. To further demonstrate the practical applicability of our technique we show that our technique can improve performance, with minimal code changes, for mesh smoothing, mesh parametrization, volumetric deformation, optical flow, and computation of the Laplace operator.en_US
dc.description.number5
dc.description.sectionheadersOptimization
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume39
dc.identifier.doi10.1111/cgf.14080
dc.identifier.issn1467-8659
dc.identifier.pages209-219
dc.identifier.urihttps://doi.org/10.1111/cgf.14080
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14080
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleEGGS: Sparsity-Specific Code Generationen_US
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