Basic Linear Algebra for Sparse Matrices on NVIDIA GPUs


The cuSPARSE library provides GPU-accelerated basic linear algebra subroutines for sparse matrices that perform significantly faster than CPU-only alternatives. It provides functionality that can be used to build GPU accelerated solvers. cuSPARSE is widely used by engineers and scientists working on applications such as machine learning, computational fluid dynamics, seismic exploration and computational sciences. Using cuSPARSE, applications automatically benefit from regular performance improvements and new GPU architectures. The cuSPARSE library is included in both the NVIDIA HPC SDK and the CUDA Toolkit.

Explore what’s new in the latest release...

cuSPARSE Performance

The cuSPARSE library is highly optimized for performance on NVIDIA GPUs, with SpMM performance 30-150X faster than CPU-only alternatives.

cuSPARSE Key Features

  • Support for dense, COO, CSR, CSC, and Blocked CSR sparse matrix formats
  • Full suite of sparse routines covering sparse vector x dense vector operations, sparse matrix x dense vector operations, and sparse matrix x dense matrix operations.
  • Routines for sparse matrix x sparse matrix addition and multiplication
  • Generic high-performance APIs for sparse-dense vector multiplication (SpVV), sparse matrix-dense vector multiplication (SpMV), and sparse matrix-dense matrix multiplication (SpMM)
  • ILU0 and IC0 preconditioners