DOI

SPbLA is a sparse Boolean linear algebra primitives and operations for GPGPU computations. It comes as a stand-alone self-sufficient library with C API for high-performance computing with multiple backends for Nvidia Cuda, OpenCL and CPU-only platforms. The library has PyPI pyspbla package for work within a Python runtime. The primary library primitive is a sparse matrix of Boolean values. The library provides the most popular operations for matrix manipulation, such as construction from values, transpose, sub-matrix extraction, matrix-to-vector reduce, matrix-matrix element-wise addition, multiplication and Kronecker product.
Translated title of the contributionБиблиотека примитивов разреженной булевой алгебры для вычислений на ГПУ
Original languageEnglish
Article number76
Pages (from-to)1
Number of pages5
JournalThe Journal of Open Source Software
Volume7
Issue number76
DOIs
StatePublished - 20 Aug 2022

    Research areas

  • C, C++, python, sparse matrix, graph analysis, graph algorithms, linear algebra, nvidia cuda, OpenCL

ID: 97999105