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

Abstract

Sparse matrices are widely applicable in data analysis while the theory of matrix processing is well-established. There are a wide range of algorithms for basic operations such as matrix-matrix and matrix-vector multiplication, factorization, etc. To facilitate data analysis, GraphBLAS API provides a set of building blocks and allows for reducing algorithms to sparse linear algebra operations. While GPGPU utilization for high-performance linear algebra is common, the high complexity of GPGPU programming makes the implementation of GraphBLAS API on GPGPU challenging. In this work, we present a GPGPU library of sparse operations for an important case — Boolean algebra. The library is based on modern algorithms for sparse matrix processing. We provide a Python wrapper for the library to simplify its use in applied solutions. Our evaluation shows that operations specialized for Boolean matrices can be up to 5 times faster and consume up to 4 times less memory than generic, not the Boolean optimized, operations from modern libraries. We hope that our results help to move the development of a GPGPU version of GraphBLAS API forward.