CUSPARSE

The CUSPARSE library contains a set of basic linear algebra subroutines used for handling sparse matrices. It is implemented on top of the NVIDIA® CUDA™ runtime (which is part of the CUDA Toolkit) and is designed to be called from C and C++. The library routines can be classified into four categories: Level 1: operations between a vector in sparse format and a vector in dense format; Level 2: operations between a matrix in sparse format and a vector in dense format; Level 3: operations between a matrix in sparse format and a set of vectors in dense format (which can also usually be viewed as a dense tall matrix); Conversion: operations that allow conversion between different matrix formats.


References in zbMATH (referenced in 42 articles )

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  1. Arndt, Daniel; Bangerth, Wolfgang; Blais, Bruno; Clevenger, Thomas C.; Fehling, Marc; Grayver, Alexander V.; Heister, Timo; Heltai, Luca; Kronbichler, Martin; Maier, Matthias; Munch, Peter; Pelteret, Jean-Paul; Rastak, Reza; Tomas, Ignacio; Turcksin, Bruno; Wang, Zhuoran; Wells, David: The deal.II library, version 9.2 (2020)
  2. Khimich, O. M.; Popov, O. V.; Chistyakov, O. V.; Sidoruk, V. A.: A parallel algorithm for solving a partial eigenvalue problem for block-diagonal bordered matrices (2020)
  3. Sashikumaar Ganesan, Manan Shah: SParSH-AMG: A library for hybrid CPU-GPU algebraic multigrid and preconditioned iterative methods (2020) arXiv
  4. Arndt, Daniel; Bangerth, Wolfgang; Clevenger, Thomas C.; Davydov, Denis; Fehling, Marc; Garcia-Sanchez, Daniel; Harper, Graham; Heister, Timo; Heltai, Luca; Kronbichler, Martin; Kynch, Ross Maguire; Maier, Matthias; Pelteret, Jean-Paul; Turcksin, Bruno; Wells, David: The deal.II library, Version 9.1 (2019)
  5. Bernaschi, Massimo; Carrozzo, Mauro; Franceschini, Andrea; Janna, Carlo: A dynamic pattern factored sparse approximate inverse preconditioner on graphics processing units (2019)
  6. Cheng, Xuan; Zeng, Ming; Lin, Jinpeng; Wu, Zizhao; Liu, Xinguo: Efficient (L_0) resampling of point sets (2019)
  7. Jaber J. Hasbestan, Inanc Senocak: PittPack: An Open-Source Poisson’s Equation Solver for Extreme-Scale Computing with Accelerators (2019) arXiv
  8. Li, Ruipeng; Xi, Yuanzhe; Erlandson, Lucas; Saad, Yousef: The eigenvalues slicing library (EVSL): algorithms, implementation, and software (2019)
  9. Mozaffar, Mojtaba; Ndip-Agbor, Ebot; Lin, Stephen; Wagner, Gregory J.; Ehmann, Kornel; Cao, Jian: Acceleration strategies for explicit finite element analysis of metal powder-based additive manufacturing processes using graphical processing units (2019)
  10. Rakhsha, M.; Pazouki, A.; Serban, R.; Negrut, D.: Using a half-implicit integration scheme for the SPH-based solution of fluid-solid interaction problems (2019)
  11. Sistla, Meghana Aparna; Nandivada, V. Krishna: Graph coloring using GPUs (2019)
  12. Alzetta, Giovanni; Arndt, Daniel; Bangerth, Wolfgang; Boddu, Vishal; Brands, Benjamin; Davydov, Denis; Gassmöller, Rene; Heister, Timo; Heltai, Luca; Kormann, Katharina; Kronbichler, Martin; Maier, Matthias; Pelteret, Jean-Paul; Turcksin, Bruno; Wells, David: The deal.II library, version 9.0 (2018)
  13. Gremse, Felix; Küpper, Kerstin; Naumann, Uwe: Memory-efficient sparse matrix-matrix multiplication by row merging on many-core architectures (2018)
  14. Pikle, Nileshchandra K.; Sathe, Shailesh R.; Vyavhare, Arvind Y.: GPGPU-based parallel computing applied in the FEM using the conjugate gradient algorithm: a review (2018)
  15. Tan, Guangming; Liu, Junhong; Li, Jiajia: Design and implementation of adaptive SpMV library for multicore and many-core architecture (2018)
  16. Yang, Wangdong; Li, Kenli; Li, Keqin: A parallel computing method using blocked format with optimal partitioning for SpMV on GPU (2018)
  17. Aurentz, Jared L.; Kalantzis, Vassilis; Saad, Yousef: Cucheb: a GPU implementation of the filtered Lanczos procedure (2017)
  18. Filippone, Salvatore; Cardellini, Valeria; Barbieri, Davide; Fanfarillo, Alessandro: Sparse matrix-vector multiplication on GPGPUs (2017)
  19. Gao, Jiaquan; Wu, Kesong; Wang, Yushun; Qi, Panpan; He, Guixia: GPU-accelerated preconditioned GMRES method for two-dimensional Maxwell’s equations (2017)
  20. Li, Ang; Serban, Radu; Negrut, Dan: Analysis of a splitting approach for the parallel solution of linear systems on GPU cards (2017)

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