Comparison of SPMV performance on matrices with different matrix format using CUSP, cuSPARSE and ViennaCL
Simulation Based Engineering Lab, University of Wisconsin – Madison
University of Wisconsin – Madison, Technical report TR-2015-02, 2015
@article{li2015comparison,
title={Comparison of SPMV performance on matrices with different matrix format using CUSP, cuSPARSE and ViennaCL},
author={Li, Ang and Hammad Mazhar, Radu Serban and Negrut, Dan},
year={2015}
}
ViennaCL is a free open-source linear algebra library for computations on many-core architectures (GPUs, MIC) and multi-core CPUs. The library is written in C++ and supports CUDA, OpenCL, and OpenMP. In addition to core functionality and many other features including BLAS level 1-3 support and iterative solvers, the latest release family ViennaCL 1.6.x provides fast pipelined iterative solvers including fast sparse matrix-vector products based on CSR-adaptive, a new fully HTML-based documentation, and a new sparse matrix type. Also, a Python wrapper named PyViennaCL is available [2]. CUSP is an open source C++ library of generic parallel algorithms for sparse linear algebra and graph computations on CUDA architecture GPUs. CUSP provides a flexible, high-level interface for manipulating sparse matrices and solving sparse linear systems [5]. The NVIDIA CUDA Sparse Matrix library (cuSPARSE) provides a collection of basic linear algebra subroutines used for sparse matrices that delivers up to 8x faster performance than the latest MKL. The cuSPARSE library is designed to be called from C or C++, and the latest release includes a sparse triangular solver [1].
February 22, 2015 by hgpu
