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- W4321650141 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." @default.
- W4321650141 created "2023-02-24" @default.
- W4321650141 creator A5076562224 @default.
- W4321650141 creator A5082922326 @default.
- W4321650141 date "2023-02-22" @default.
- W4321650141 modified "2023-09-27" @default.
- W4321650141 title "Engineering a Distributed-Memory Triangle Counting Algorithm" @default.
- W4321650141 doi "https://doi.org/10.48550/arxiv.2302.11443" @default.
- W4321650141 hasPublicationYear "2023" @default.
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