GitHub - cusplibrary/cusplibrary: CUSP : A C++ Templated Sparse Matrix Library

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For more information, see the project documentation at CUSP Website.

A Simple Example

#include <cuda.h>
#include <thrust/version.h>

#include <cusp/version.h>
#include <cusp/hyb_matrix.h>
#include <cusp/io/matrix_market.h>
#include <cusp/krylov/cg.h>

#include <iostream>

int main(void)
{
    int cuda_major =  CUDA_VERSION / 1000;
    int cuda_minor = (CUDA_VERSION % 1000) / 10;
    int thrust_major = THRUST_MAJOR_VERSION;
    int thrust_minor = THRUST_MINOR_VERSION;
    int cusp_major = CUSP_MAJOR_VERSION;
    int cusp_minor = CUSP_MINOR_VERSION;
    std::cout << "CUDA   v" << cuda_major   << "." << cuda_minor   << std::endl;
    std::cout << "Thrust v" << thrust_major << "." << thrust_minor << std::endl;
    std::cout << "Cusp   v" << cusp_major   << "." << cusp_minor   << std::endl;

    // create an empty sparse matrix structure (HYB format)
    cusp::hyb_matrix<int, float, cusp::device_memory> A;

    // load a matrix stored in Matrix-Market format
    cusp::io::read_matrix_market_file(A, "./testing/data/laplacian/5pt_10x10.mtx");

    // allocate storage for solution (x) and right hand side (b)
    cusp::array1d<float, cusp::device_memory> x(A.num_rows, 0);
    cusp::array1d<float, cusp::device_memory> b(A.num_rows, 1);

    // solve the linear system A * x = b with the conjugate gradient method
    cusp::krylov::cg(A, x, b);

    return 0;
}

CUSP is a header-only library. To compile this example clone both CUSP and Nvidia/cccl:

git@github.com:cusplibrary/cusplibrary.git
cd cusplibrary
git clone git@github.com:NVIDIA/cccl.git
nvcc -Icccl/thrust -Icccl/libcudacxx/include -Icccl/cub -I. example.cu -o example

Status

CUSP is up-to-date with CCCL v2.8.5. It has been tested with CUDA 13.

To build:

First point to CCCL (and to THRUST if using a gcc build). For example with CCCL in your home directory:

export CCCL_PATH=$HOME/cccl
export THRUST_PATH=$HOME/cccl/thrust

Next, you must have a recent version of Scons for the build:

To build/run with gcc, use an omp backend with CUSP blas:

scons compiler=gcc backend=omp deviceblas=cusp mode=release -j 16

To build/run with nvcc, use:

(see https://arnon.dk/matching-sm-architectures-arch-and-gencode-for-various-nvidia-cards/ for a full list)

A single test can be run with scons single_test=poisson, for example.

Examples can be found in examples and performance results can be found in performance with similar scons instructions.

Contributors

CUSP is developed as an open-source project with NVIDIA Research. Nathan Bell was the original creator. It is currently developed by Steven Dalton and Luke Olson

CUSP is available under the Apache v2.0 open source LICENSE

Citing

@MISC{Cusp,
  author = "Steven Dalton and Nathan Bell and Luke Olson and Michael Garland",
  title = "Cusp: Generic Parallel Algorithms for Sparse Matrix and Graph Computations",
  year = "2026",
  url = "https://github.com/cusplibrary/cusplibrary",
  note = "Version 0.6.0"
}