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.
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References in zbMATH (referenced in 47 articles )
Showing results 41 to 47 of 47.
- Koza, Zbigniew; Matyka, Maciej; Mirosław, Łukasz; Poła, Jakub: Sparse matrix-vector product (2014)
- Oyarzun, G.; Borrell, R.; Gorobets, A.; Oliva, A.: MPI-CUDA sparse matrix-vector multiplication for the conjugate gradient method with an approximate inverse preconditioner (2014)
- Demidov, Denis; Ahnert, Karsten; Rupp, Karl; Gottschling, Peter: Programming CUDA and OpenCL: a case study using modern C++ libraries (2013)
- Knepley, Matthew G.; Terrel, Andy R.: Finite element integration on GPGPUs (2013)
- Müller, Eike; Guo, Xu; Scheichl, Robert; Shi, Sinan: Matrix-free GPU implementation of a preconditioned conjugate gradient solver for anisotropic elliptic PDEs (2013)
- Galiano, V.; Migallón, H.; Migallón, V.; Penadés, J.: GPU-based parallel algorithms for sparse nonlinear systems (2012) ioport
- Oberhuber, Tomáš; Heller, Martin: Improved row-grouped CSR format for storing of sparse matrices on GPU (2012)