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 29 articles )

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  1. 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)
  2. Gremse, Felix; Küpper, Kerstin; Naumann, Uwe: Memory-efficient sparse matrix-matrix multiplication by row merging on many-core architectures (2018)
  3. 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)
  4. Yang, Wangdong; Li, Kenli; Li, Keqin: A parallel computing method using blocked format with optimal partitioning for SpMV on GPU (2018)
  5. Aurentz, Jared L.; Kalantzis, Vassilis; Saad, Yousef: Cucheb: a GPU implementation of the filtered Lanczos procedure (2017)
  6. Filippone, Salvatore; Cardellini, Valeria; Barbieri, Davide; Fanfarillo, Alessandro: Sparse matrix-vector multiplication on GPGPUs (2017)
  7. Gao, Jiaquan; Wu, Kesong; Wang, Yushun; Qi, Panpan; He, Guixia: GPU-accelerated preconditioned GMRES method for two-dimensional Maxwell’s equations (2017)
  8. Li, Ang; Serban, Radu; Negrut, Dan: Analysis of a splitting approach for the parallel solution of linear systems on GPU cards (2017)
  9. Anzt, Hartwig; Chow, Edmond; Saak, Jens; Dongarra, Jack: Updating incomplete factorization preconditioners for model order reduction (2016)
  10. Bernaschi, Massimo; Bisson, Mauro; Fantozzi, Carlo; Janna, Carlo: A factored sparse approximate inverse preconditioned conjugate gradient solver on graphics processing units (2016)
  11. Bertaccini, Daniele; Filippone, Salvatore: Sparse approximate inverse preconditioners on high performance GPU platforms (2016)
  12. D’Ambra, Pasqua; Filippone, Salvatore: A parallel generalized relaxation method for high-performance image segmentation on GPUs (2016)
  13. Gao, Jiaquan; Qi, Panpan; He, Guixia: Efficient CSR-based sparse matrix-vector multiplication on GPU (2016)
  14. László, Endre; Giles, Mike; Appleyard, Jeremy: Manycore algorithms for batch scalar and block tridiagonal solvers (2016)
  15. 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)
  16. Gremse, Felix; Höfter, Andreas; Schwen, Lars Ole; Kiessling, Fabian; Naumann, Uwe: GPU-accelerated sparse matrix-matrix multiplication by iterative row merging (2015)
  17. Magoulès, Frédéric; Ahamed, Abal-Kassim Cheik; Putanowicz, Roman: Auto-tuned Krylov methods on cluster of graphics processing unit (2015)
  18. Mironowicz, P.; Dziekonski, A.; Mrozowski, M.: A task-scheduling approach for efficient sparse symmetric matrix-vector multiplication on a GPU (2015)
  19. Naumov, M.; Arsaev, M.; Castonguay, P.; Cohen, J.; Demouth, J.; Eaton, J.; Layton, S.; Markovskiy, N.; Reguly, I.; Sakharnykh, N.; Sellappan, V.; Strzodka, R.: AmgX: a library for GPU accelerated algebraic multigrid and preconditioned iterative methods (2015)
  20. Wong, J.; Kuhl, E.; Darve, E.: A new sparse matrix vector multiplication graphics processing unit algorithm designed for finite element problems (2015)

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