Batch Updates of Distributed Streaming Graphs using Linear Algebra

We develop a distributed-memory parallel algorithm for performing batch updates on streaming graphs, where vertices and edges are continuously added or removed. Our algorithm leverages distributed sparse matrices as the core data structures, utilizing equivalent sparse matrix operations to execute g...

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Vydané v:SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis s. 645 - 649
Hlavní autori: Hassani, Elaheh, Hussain, Md Taufique, Azad, Ariful
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 17.11.2024
Predmet:
batch graph updates
Conferences
Data structures
distributed-memory algorithms
High performance computing
Linear algebra
Parallel algorithms
parallel computing
parallel graph algorithms
scalability in graph processing
Sparse matrices
streaming graphs
Supercomputers
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Abstract We develop a distributed-memory parallel algorithm for performing batch updates on streaming graphs, where vertices and edges are continuously added or removed. Our algorithm leverages distributed sparse matrices as the core data structures, utilizing equivalent sparse matrix operations to execute graph updates. By reducing unnecessary communication among processes and employing shared-memory parallelism, we accelerate updates of distributed graphs. Additionally, we maintain a balanced load in the output matrix by permuting the resultant matrix during the update process. We demonstrate that our streaming update algorithm is at least 25 times faster than alternative linear-algebraic methods and scales linearly up to 4,096 cores (32 nodes) on a Cray EX supercomputer.
AbstractList We develop a distributed-memory parallel algorithm for performing batch updates on streaming graphs, where vertices and edges are continuously added or removed. Our algorithm leverages distributed sparse matrices as the core data structures, utilizing equivalent sparse matrix operations to execute graph updates. By reducing unnecessary communication among processes and employing shared-memory parallelism, we accelerate updates of distributed graphs. Additionally, we maintain a balanced load in the output matrix by permuting the resultant matrix during the update process. We demonstrate that our streaming update algorithm is at least 25 times faster than alternative linear-algebraic methods and scales linearly up to 4,096 cores (32 nodes) on a Cray EX supercomputer.
Author Azad, Ariful
Hassani, Elaheh
Hussain, Md Taufique
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  organization: Indiana University,Dept. of Intelligent Systems Engineering,Bloomington,IN,USA
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  givenname: Md Taufique
  surname: Hussain
  fullname: Hussain, Md Taufique
  email: mth@iu.edu
  organization: Indiana University,Dept. of Intelligent Systems Engineering,Bloomington,IN,USA
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  givenname: Ariful
  surname: Azad
  fullname: Azad, Ariful
  email: azad@iu.edu
  organization: Indiana University,Dept. of Intelligent Systems Engineering,Bloomington,IN,USA
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Snippet We develop a distributed-memory parallel algorithm for performing batch updates on streaming graphs, where vertices and edges are continuously added or...
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StartPage 645
SubjectTerms batch graph updates
Conferences
Data structures
distributed-memory algorithms
High performance computing
Linear algebra
Parallel algorithms
parallel computing
parallel graph algorithms
scalability in graph processing
Sparse matrices
streaming graphs
Supercomputers
Title Batch Updates of Distributed Streaming Graphs using Linear Algebra
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