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 52 articles )

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  1. Gremse, Felix; Küpper, Kerstin; Naumann, Uwe: Memory-efficient sparse matrix-matrix multiplication by row merging on many-core architectures (2018)
  2. 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)
  3. Tan, Guangming; Liu, Junhong; Li, Jiajia: Design and implementation of adaptive SpMV library for multicore and many-core architecture (2018)
  4. Yang, Wangdong; Li, Kenli; Li, Keqin: A parallel computing method using blocked format with optimal partitioning for SpMV on GPU (2018)
  5. Aliaga, José I.; Bollhöfer, Matthias; Dufrechou, Ernesto; Ezzatti, Pablo; Quintana-Ortí, Enrique S.: A data-parallel ILUPACK for sparse general and symmetric indefinite linear systems (2017)
  6. Aurentz, Jared L.; Kalantzis, Vassilis; Saad, Yousef: Cucheb: a GPU implementation of the filtered Lanczos procedure (2017)
  7. Filippone, Salvatore; Cardellini, Valeria; Barbieri, Davide; Fanfarillo, Alessandro: Sparse matrix-vector multiplication on GPGPUs (2017)
  8. Gao, Jiaquan; Wu, Kesong; Wang, Yushun; Qi, Panpan; He, Guixia: GPU-accelerated preconditioned GMRES method for two-dimensional Maxwell’s equations (2017)
  9. Li, Ang; Serban, Radu; Negrut, Dan: Analysis of a splitting approach for the parallel solution of linear systems on GPU cards (2017)
  10. Oyarzun, Guillermo; Borrell, Ricard; Gorobets, Andrey; Oliva, Assensi: Portable implementation model for CFD simulations. Application to hybrid CPU/GPU supercomputers (2017)
  11. Anzt, Hartwig; Chow, Edmond; Saak, Jens; Dongarra, Jack: Updating incomplete factorization preconditioners for model order reduction (2016)
  12. Bernaschi, Massimo; Bisson, Mauro; Fantozzi, Carlo; Janna, Carlo: A factored sparse approximate inverse preconditioned conjugate gradient solver on graphics processing units (2016)
  13. Bertaccini, Daniele; Filippone, Salvatore: Sparse approximate inverse preconditioners on high performance GPU platforms (2016)
  14. D’Ambra, Pasqua; Filippone, Salvatore: A parallel generalized relaxation method for high-performance image segmentation on GPUs (2016)
  15. Gao, Jiaquan; Qi, Panpan; He, Guixia: Efficient CSR-based sparse matrix-vector multiplication on GPU (2016)
  16. László, Endre; Giles, Mike; Appleyard, Jeremy: Manycore algorithms for batch scalar and block tridiagonal solvers (2016)
  17. D’Amore, L.; Laccetti, G.; Romano, D.; Scotti, G.; Murli, A.: Towards a parallel component in a GPU-CUDA environment: a case study with the L-BFGS Harwell routine (2015)
  18. Gremse, Felix; Höfter, Andreas; Schwen, Lars Ole; Kiessling, Fabian; Naumann, Uwe: GPU-accelerated sparse matrix-matrix multiplication by iterative row merging (2015)
  19. Magoulès, Frédéric; Ahamed, Abal-Kassim Cheik; Putanowicz, Roman: Auto-tuned Krylov methods on cluster of graphics processing unit (2015)
  20. Mironowicz, P.; Dziekonski, A.; Mrozowski, M.: A task-scheduling approach for efficient sparse symmetric matrix-vector multiplication on a GPU (2015)