Affiliation:
1. Texas A8M University, TX, USA
Abstract
SuiteSparse:GraphBLAS is a full implementation of the GraphBLAS standard, which defines a set of sparse matrix operations on an extended algebra of semirings using an almost unlimited variety of operators and types. When applied to sparse adjacency matrices, these algebraic operations are equivalent to computations on graphs. GraphBLAS provides a powerful and expressive framework for creating graph algorithms based on the elegant mathematics of sparse matrix operations on a semiring. An overview of the GraphBLAS specification is given, followed by a description of the key features and performance of its implementation in the SuiteSparse:GraphBLAS package.
Funder
MIT Lincoln Laboratory
Redis Labs
National Science Foundation
Intel
NVIDIA
IBM
Publisher
Association for Computing Machinery (ACM)
Subject
Applied Mathematics,Software
Reference22 articles.
1. GraphPad: Optimized Graph Primitives for Parallel and Distributed Platforms
2. Parallel Triangle Counting and Enumeration Using Matrix Algebra
3. On the representation and multiplication of hypersparse matrices
4. The Combinatorial BLAS: design, implementation, and applications
5. A. Buluç T. Mattson S. McMillan J. Moreira and C. Yang. 2017. The GraphBLAS C API Specification. Technical Report. http://graphblas.org/. A. Buluç T. Mattson S. McMillan J. Moreira and C. Yang. 2017. The GraphBLAS C API Specification. Technical Report. http://graphblas.org/.
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