Engineering a Distributed-Memory Triangle Counting Algorithm
Counting triangles in a graph and incident to each vertex is a fundamental and frequently considered task of graph analysis. We consider how to efficiently do this for huge graphs using massively parallel distributed-memory machines. Unsurprisingly, the main issue is to reduce communication between...
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| Vydáno v: | Proceedings - IEEE International Parallel and Distributed Processing Symposium s. 702 - 712 |
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| Hlavní autoři: | , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
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01.05.2023
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| ISSN: | 1530-2075 |
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| Abstract | Counting triangles in a graph and incident to each vertex is a fundamental and frequently considered task of graph analysis. We consider how to efficiently do this for huge graphs using massively parallel distributed-memory machines. Unsurprisingly, the main issue is to reduce communication between processors. We achieve this by counting locally whenever possible and reducing the amount of information that needs to be sent in order to handle (possible) nonlocal triangles. We also achieve linear memory requirements despite superlinear communication volume by introducing a new asynchronous sparse-all-to-all operation. Furthermore, we dramatically reduce startup overheads by allowing this communication to use indirect routing. Our algorithms scale (at least) up to 32 768 cores and are up to 18 times faster than the previous state of the art. |
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| AbstractList | Counting triangles in a graph and incident to each vertex is a fundamental and frequently considered task of graph analysis. We consider how to efficiently do this for huge graphs using massively parallel distributed-memory machines. Unsurprisingly, the main issue is to reduce communication between processors. We achieve this by counting locally whenever possible and reducing the amount of information that needs to be sent in order to handle (possible) nonlocal triangles. We also achieve linear memory requirements despite superlinear communication volume by introducing a new asynchronous sparse-all-to-all operation. Furthermore, we dramatically reduce startup overheads by allowing this communication to use indirect routing. Our algorithms scale (at least) up to 32 768 cores and are up to 18 times faster than the previous state of the art. |
| Author | Uhl, Tim Niklas Sanders, Peter |
| Author_xml | – sequence: 1 givenname: Peter surname: Sanders fullname: Sanders, Peter email: sanders@kit.edu organization: Karlsruhe Institute of Technology,Institute of Theoretical Informatics,Karlsruhe,Germany – sequence: 2 givenname: Tim Niklas surname: Uhl fullname: Uhl, Tim Niklas email: uhl@kit.edu organization: Karlsruhe Institute of Technology,Institute of Theoretical Informatics,Karlsruhe,Germany |
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| Snippet | Counting triangles in a graph and incident to each vertex is a fundamental and frequently considered task of graph analysis. We consider how to efficiently do... |
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| StartPage | 702 |
| SubjectTerms | clustering coefficient Distributed processing distributed-memory algorithm graph analysis Memory management MPI Program processors Routing Task analysis triangle counting |
| Title | Engineering a Distributed-Memory Triangle Counting Algorithm |
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