SPbLA: The Library of GPGPU-powered Sparse Boolean Linear Algebra Operations

Egor Orachev, Maria Karpenko, Pavel Alimov, Semyon Grigorev
2022 Journal of Open Source Software  
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 (Orachev et al., 2021) 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
more » ... nstruction from values, transpose, sub-matrix extraction, matrix-to-vector reduce, matrix-matrix element-wise addition, multiplication and Kronecker product.
doi:10.21105/joss.03743 fatcat:2w5erbiss5bk5g4xeekowzadzy