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 29 articles , 1 standard article )

Showing results 1 to 20 of 29.
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  1. Bertaccini, Daniele; Durastante, Fabio: Iterative methods and preconditioning for large and sparse linear systems with applications (2018)
  2. Cipolla, Stefano; Durastante, Fabio: Fractional PDE constrained optimization: an optimize-then-discretize approach with L-BFGS and approximate inverse preconditioning (2018)
  3. Gremse, Felix; Küpper, Kerstin; Naumann, Uwe: Memory-efficient sparse matrix-matrix multiplication by row merging on many-core architectures (2018)
  4. Bertaccini, Daniele; Durastante, Fabio: Solving mixed classical and fractional partial differential equations using short-memory principle and approximate inverses (2017)
  5. Filippone, Salvatore; Cardellini, Valeria; Barbieri, Davide; Fanfarillo, Alessandro: Sparse matrix-vector multiplication on GPGPUs (2017)
  6. Li, Ang; Serban, Radu; Negrut, Dan: Analysis of a splitting approach for the parallel solution of linear systems on GPU cards (2017)
  7. Zhao, Jia; Wang, Qi: Three-dimensional numerical simulations of biofilm dynamics with quorum sensing in a flow cell (2017)
  8. Cuvelier, François; Japhet, Caroline; Scarella, Gilles: An efficient way to assemble finite element matrices in vector languages (2016)
  9. De La Cruz, Luis M.; Ramos, Eduardo: General template units for the finite volume method in box-shaped domains (2016)
  10. 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)
  11. Sierakowski, Adam J.: GPU-centric resolved-particle disperse two-phase flow simulation using the Physalis method (2016)
  12. Dalton, Steven; Olson, Luke; Bell, Nathan: Optimizing sparse matrix-matrix multiplication for the GPU (2015)
  13. Gremse, Felix; Höfter, Andreas; Schwen, Lars Ole; Kiessling, Fabian; Naumann, Uwe: GPU-accelerated sparse matrix-matrix multiplication by iterative row merging (2015)
  14. Magoulès, Frédéric; Ahamed, Abal-Kassim Cheik; Putanowicz, Roman: Auto-tuned Krylov methods on cluster of graphics processing unit (2015)
  15. Serban, Radu; Melanz, Daniel; Li, Ang; Stanciulescu, Ilinca; Jayakumar, Paramsothy; Negrut, Dan: A GPU-based preconditioned Newton-Krylov solver for flexible multibody dynamics (2015)
  16. Chen, Zhangxin; Liu, Hui; Yu, Song; Hsieh, Ben; Shao, Lei: GPU-based parallel reservoir simulators (2014)
  17. Dastgeer, Usman; Li, Lu; Kessler, Christoph: The PEPPHER composition tool: performance-aware composition for GPU-based systems (2014) ioport
  18. Einkemmer, L.; Wiesenberger, M.: A conservative discontinuous Galerkin scheme for the 2D incompressible Navier-Stokes equations (2014)
  19. King, James; Yakovlev, Sergey; Fu, Zhisong; Kirby, Robert M.; Sherwin, Spencer J.: Exploiting batch processing on streaming architectures to solve 2D elliptic finite element problems: a hybridized discontinuous Galerkin (HDG) case study (2014)
  20. Koza, Zbigniew; Matyka, Maciej; Mirosław, Łukasz; Poła, Jakub: Sparse matrix-vector product (2014)

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