GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU

DOI10.1145/3466795OpenAlexW2964357930WikidataQ113309871 ScholiaQ113309871MaRDI QIDQ5066588

Aydın Buluç, John D. Owens, Carl Yang

Publication date: 29 March 2022

Published in: ACM Transactions on Mathematical Software (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1145/3466795

zbMATH Keywords

graph algorithmGPUsparse linear algebramatrix multiply

Mathematics Subject Classification ID

Numerical analysis (65-XX)

Related Items (2)

GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU ⋮ GraphBLAST

Uses Software

• SuiteSparse
• SparseMatrix
• NumPy
• CUSP
• Thrust
• Pregel
• PowerGraph
• nvGRAPH

Cites Work

• Structure prediction and computation of sparse matrix products
• Adaptive methods for the computation of PageRank
• Better size estimation for sparse matrix products
• Organization and maintenance of large ordered indexes
• Optimizing Sparse Matrix—Matrix Multiplication for the GPU
• The university of Florida sparse matrix collection
• Sparse Matrix-Vector Multiplication on GPGPUs
• Engineering Route Planning Algorithms
• An O(logn) parallel connectivity algorithm
• Two Fast Algorithms for Sparse Matrices: Multiplication and Permuted Transposition
• Algorithm 1000
• GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU
• An in-depth analysis of stochastic Kronecker graphs
• Discrete range searching primitive for the GPU and its applications
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