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 22 articles )

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  1. Yang, Wangdong; Li, Kenli; Li, Keqin: A parallel computing method using blocked format with optimal partitioning for SpMV on GPU (2018)
  2. Li, Ang; Serban, Radu; Negrut, Dan: Analysis of a splitting approach for the parallel solution of linear systems on GPU cards (2017)
  3. Anzt, Hartwig; Chow, Edmond; Saak, Jens; Dongarra, Jack: Updating incomplete factorization preconditioners for model order reduction (2016)
  4. Bernaschi, Massimo; Bisson, Mauro; Fantozzi, Carlo; Janna, Carlo: A factored sparse approximate inverse preconditioned conjugate gradient solver on graphics processing units (2016)
  5. Bertaccini, Daniele; Filippone, Salvatore: Sparse approximate inverse preconditioners on high performance GPU platforms (2016)
  6. D’Ambra, Pasqua; Filippone, Salvatore: A parallel generalized relaxation method for high-performance image segmentation on GPUs (2016)
  7. László, Endre; Giles, Mike; Appleyard, Jeremy: Manycore algorithms for batch scalar and block tridiagonal solvers (2016)
  8. Li, Zheng; Feng, Chunsheng; Zhang, Chensong: An efficient SpMV for petroleum reservoir simulation on GPUs (2016)
  9. 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)
  10. Gremse, Felix; Höfter, Andreas; Schwen, Lars Ole; Kiessling, Fabian; Naumann, Uwe: GPU-accelerated sparse matrix-matrix multiplication by iterative row merging (2015)
  11. Magoulès, Frédéric; Ahamed, Abal-Kassim Cheik; Putanowicz, Roman: Auto-tuned Krylov methods on cluster of graphics processing unit (2015)
  12. Mironowicz, P.; Dziekonski, A.; Mrozowski, M.: A task-scheduling approach for efficient sparse symmetric matrix-vector multiplication on a GPU (2015)
  13. 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)
  14. Wong, J.; Kuhl, E.; Darve, E.: A new sparse matrix vector multiplication graphics processing unit algorithm designed for finite element problems (2015)
  15. Birk, Matthias; Dapp, Robin; Ruiter, N.V.; Becker, J.: GPU-based iterative transmission reconstruction in 3D ultrasound computer tomography (2014) ioport
  16. Chang, Li-Wen; Hwu, Wen-Mei W.: A guide for implementing tridiagonal solvers on GPUs (2014)
  17. Gao, Jiaquan; Liang, Ronghua; Wang, Jun: Research on the conjugate gradient algorithm with a modified incomplete Cholesky preconditioner on GPU (2014) ioport
  18. Koza, Zbigniew; Matyka, Maciej; Mirosław, Łukasz; Poła, Jakub: Sparse matrix-vector product (2014)
  19. Demidov, Denis; Ahnert, Karsten; Rupp, Karl; Gottschling, Peter: Programming CUDA and OpenCL: a case study using modern C++ libraries (2013)
  20. Knepley, Matthew G.; Terrel, Andy R.: Finite element integration on GPGPUs (2013)

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