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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Veröffentlicht in:Proceedings - IEEE International Parallel and Distributed Processing Symposium S. 702 - 712
Hauptverfasser: Sanders, Peter, Uhl, Tim Niklas
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.05.2023
Schlagworte:
clustering coefficient
Distributed processing
distributed-memory algorithm
graph analysis
Memory management
MPI
Program processors
Routing
Task analysis
triangle counting
ISSN:1530-2075
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Zusammenfassung: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.
ISSN:1530-2075
DOI:10.1109/IPDPS54959.2023.00076