The GraphBLAS Forum is an open effort to define standard building blocks for graph algorithms in the language of linear algebra.

We believe that the state of the art in constructing a large collection of graph algorithms in terms of linear algebraic operations is mature enough to support the emergence of a standard set of primitive building blocks. We believe that it is critical to move quickly and define such a standard, thereby freeing up researchers to innovate and diversify at the level of higher level algorithms and graph analytics applications. This effort was inspired by the Basic Linear Algebra Subprograms (BLAS) of dense Linear Algebra, and hence our working name for this standard is “the GraphBLAS”.

A key insight behind this work is that when a graph is represented by a sparse incidence or adjacency matrix, sparse matrix-vector multiplication is a step of breadth first search. By generalizing the pair of scalar operations involved in the linear algebra computations to define a semiring, we can extend the range of these primitives to support a wide range of parallel graph algorithms.

The GraphBLAS Wikipedia Page

The C reference implementation is SuiteSparse:GraphBLAS.

Our 2013 manifesto for this project can be found here.

The mathematical definition of the GraphBLAS can be found here.

Background information about graphs in the language of linear algebra can be found in the book: Graph Algorithms in the Language of Linear Algebra, edited by J. Kepner and J. Gilbert, SIAM, 2011.

The Mathematics of Big Data by J. Kepner and H. Jananthan is the first book to present the common mathematical foundations of big data analysis across a range of applications and technologies.

A straw man proposal for the GraphBLAS can be found here

Gabor Szarnyas maintains a list of GraphBLAS pointers with lots of tutorial material.

Version 1.0 (provisional) of the C language API is released on May 29, 2017 at the GABB workshop here.

Version 1.1.0 (provisional) is released on November 14, 2017.

Version 1.2.0 (final) is released on May 18, 2018. It can be downloaded from here. This is only available for archival purposes, you should use the latest version 1.3.0 below.

Version 1.3.0 (final) is released on September 25, 2019. It can be downloaded from here.

• SuiteSparse Graph BLAS (Texas A&M)

• IBM GraphBLAS

• GraphBLAS Template Library (SEI/Indiana)

• GraphBLAST (UC Davis and LBNL)

• MPI/C++ Combinatorial BLAS (CombBLAS)

• Java Graphulo

• Matlab/Octave D4M

• GraphPad (Intel)

• MATLAB (comes with SuiteSparse)

• pygraphblas Python library

• grblas Python library

• pggraphblas Postgres extension

• Julia library

• RedisGraph: A Graph Database Module for Redis

• SIAM CSE'21 GraphBLAS Minisymposium Session 1

• SIAM CSE'21 GraphBLAS Minisymposium Session 2

• SIAM CSE'21 GraphBLAS Tutorial Session 1

• SIAM CSE'21 GraphBLAS Tutorial Session 2

• Graphs, Architectures, Programming, and Learning (GrAPL) @IPDPS

• High Performance Extreme Computing (HPEC)

• GraphChallenge.org

• HPEC 2020

• HPEC 2019

• HPEC 2018

• GABB 2018 @IPDPS

• HPEC 2017

• GABB 2017 @IPDPS

• HPEC 2016

• GABB 2016 @IPDPS

• HPEC 2015

• GABB 2015 @IPDPS

• HPEC 2014

• GABB 2014 @IPDPS

• HPEC 2013

• Graph Analytics, by Tim Mattson and Henry Gabb, Intel

• Short video description of GraphBLAS

• HPEC'20 presentation on GraphBLAS in Python and MATLAB

• Presentation at UT Austin

• A good discussion thread on dropping explicit zeros (fall 2019)

If you wish to join our effort (or just watch it), please send an email message to our mailing list coordinator.

• David Bader (NJIT)

• Aydin Buluc (Berkeley Lab)

• John Gilbert (UC Santa Barbara)

• Jeremy Kepner (MIT Lincoln Laboratory Supercomputing Center)

• Tim Mattson (Intel)

• Henning Meyerhenke (KIT)