2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) 2017
DOI: 10.1109/ipdpsw.2017.117
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Design of the GraphBLAS API for C

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Cited by 76 publications
(57 citation statements)
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“…Instead, we touch on the computational building blocks for the machine learning algorithms that are commonly applied to genomic and proteomic data. A large class of machine learning methods are built on top of basic linear algebraic subroutines that are found in the modern dense BLAS [51], Sparse BLAS [52], or the GraphBLAS [53]. This relationship is illustrated in Figure 3.…”
Section: Machine Learning For Genomics and Proteomicsmentioning
confidence: 99%
“…Instead, we touch on the computational building blocks for the machine learning algorithms that are commonly applied to genomic and proteomic data. A large class of machine learning methods are built on top of basic linear algebraic subroutines that are found in the modern dense BLAS [51], Sparse BLAS [52], or the GraphBLAS [53]. This relationship is illustrated in Figure 3.…”
Section: Machine Learning For Genomics and Proteomicsmentioning
confidence: 99%
“…Multiplication on a semiring allows the user to overload scalar multiplication and addition operations and still use the same SpGEMM algorithm. Many existing SpGEMM implementations support user-defined semirings, including those that implement the GraphBLAS API (Buluç et al, 2017).…”
Section: Sparse Matrix Construction and Multiplicationmentioning
confidence: 99%
“…This section gives a brief overview of important elements of Version 1.0 of the GraphBLAS C API specification [2] in order give some context for future extensions proposed in this paper. The full specification can be downloaded from the GraphBLAS Forum website [1].…”
Section: The Graphblas C Apimentioning
confidence: 99%
“…The GraphBLAS aims to standardize the mathematical concepts [5] and the application programming interface (API) [2] for performing graph computations in the language of linear algebra [6]. The GraphBLAS C API version 1.0 has been provisionally released in May 2017 [3].…”
Section: Introductionmentioning
confidence: 99%