
@article{GraphBLAS7,
author = {Davis, Timothy A.},
title = {Algorithm 10xx: SuiteSparse:GraphBLAS: Graph Algorithms in the Language of Sparse Linear Algebra},
year = {2022},
abstract= {SuiteSparse:GraphBLAS is a full parallel 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.  A description of the parallel
implementation of SuiteSparse:GraphBLAS is given, including its novel parallel
algorithms for sparse matrix multiply, addition, element-wise multiply,
submatrix extraction and assignment, and the GraphBLAS mask/accumulator
operation.  Its performance is illustrated by solving the graph problems in the
GAP Benchmark and by comparing it with other sparse matrix libraries.},
journal = {ACM Trans. Math. Softw.},
month = {(under revision)},
note={See GraphBLAS/Doc/toms_parallel_grb2.pdf},
keywords = {GraphBLAS, Graph algorithms, sparse matrices}
}

@article{10.1145/3322125,
author = {Davis, Timothy A.},
title = {Algorithm 1000: SuiteSparse:GraphBLAS: Graph Algorithms in the Language of Sparse Linear Algebra},
year = {2019},
issue_date = {December 2019},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {45},
number = {4},
issn = {0098-3500},
url = {https://doi.org/10.1145/3322125},
doi = {10.1145/3322125},
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.},
journal = {ACM Trans. Math. Softw.},
month = {dec},
articleno = {44},
numpages = {25},
keywords = {GraphBLAS, Graph algorithms, sparse matrices}
}

