CUSP

CUSP : A C++ Templated Sparse Matrix Library. Cusp is a library for sparse linear algebra and graph computations on CUDA. Cusp provides a flexible, high-level interface for manipulating sparse matrices and solving sparse linear systems. Get Started with Cusp today!

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References in zbMATH (referenced in 40 articles , 1 standard article )

Showing results 1 to 20 of 40.
Sorted by year (citations)

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  1. Bertaccini, Daniele; Durastante, Fabio: Efficient preconditioner updates for semilinear space-time fractional reaction-diffusion equations (2019)
  2. Chen, Xiang; Wan, Decheng: Numerical simulation of three-dimensional violent free surface flows by GPU-based MPS method (2019)
  3. Cipolla, Stefano; Di Fiore, Carmine; Zellini, Paolo: Low complexity matrix projections preserving actions on vectors (2019)
  4. Demidov, D.: AMGCL: an efficient, flexible, and extensible algebraic multigrid implementation (2019)
  5. Fiore, Andrew M.; Swan, James W.: Fast Stokesian dynamics (2019)
  6. Bertaccini, Daniele; Durastante, Fabio: Iterative methods and preconditioning for large and sparse linear systems with applications (2018)
  7. Cipolla, Stefano; Durastante, Fabio: Fractional PDE constrained optimization: an optimize-then-discretize approach with L-BFGS and approximate inverse preconditioning (2018)
  8. Durastante, Fabio; Cipolla, Stefano: Fractional PDE constrained optimization: box and sparse constrained problems (2018)
  9. Gong, Yuezheng; Zhao, Jia; Yang, Xiaogang; Wang, Qi: Fully discrete second-order linear schemes for hydrodynamic phase field models of binary viscous fluid flows with variable densities (2018)
  10. Gremse, Felix; Küpper, Kerstin; Naumann, Uwe: Memory-efficient sparse matrix-matrix multiplication by row merging on many-core architectures (2018)
  11. Zanella, R.; Porta, F.; Ruggiero, V.; Zanetti, M.: Serial and parallel approaches for image segmentation by numerical minimization of a second-order functional (2018)
  12. Bertaccini, Daniele; Durastante, Fabio: Solving mixed classical and fractional partial differential equations using short-memory principle and approximate inverses (2017)
  13. Filippone, Salvatore; Cardellini, Valeria; Barbieri, Davide; Fanfarillo, Alessandro: Sparse matrix-vector multiplication on GPGPUs (2017)
  14. Li, Ang; Serban, Radu; Negrut, Dan: Analysis of a splitting approach for the parallel solution of linear systems on GPU cards (2017)
  15. Zhao, Jia; Wang, Qi: Three-dimensional numerical simulations of biofilm dynamics with quorum sensing in a flow cell (2017)
  16. Cuvelier, François; Japhet, Caroline; Scarella, Gilles: An efficient way to assemble finite element matrices in vector languages (2016)
  17. De La Cruz, Luis M.; Ramos, Eduardo: General template units for the finite volume method in box-shaped domains (2016)
  18. Gao, Jiaquan; Qi, Panpan; He, Guixia: Efficient CSR-based sparse matrix-vector multiplication on GPU (2016)
  19. He, Guixia; Gao, Jiaquan: A novel CSR-based sparse matrix-vector multiplication on GPUs (2016)
  20. Rupp, Karl; Tillet, Philippe; Rudolf, Florian; Weinbub, Josef; Morhammer, Andreas; Grasser, Tibor; Jüngel, Ansgar; Selberherr, Siegfried: ViennaCL-linear algebra library for multi- and many-core architectures (2016)

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