Triangle Counting (TC) is a basic graph mining problem with numerous applications. However, the large size of real-world graphs has a severe effect on TC performance.
This paper studies the TC algorithm from the perspective of memory utilization. We investigate the implications of the skewed degree distribution of real-world graphs on TC and make novel observations on how memory locality is negatively affected. Based on this, we introduce the LOTUS algorithm as a structure-aware TC that optimizes locality.
The evaluation on 14 real-world graphs with up to 162 billion edges and on 3 different processor architectures of up to 128 cores shows that Lotus is 2.2-5.5 times faster than previous works.
https://blogs.qub.ac.uk/GraphProcessing/LOTUS-locality-optimizing-triangle-counting
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10:50 15mTalk | LOTUS: Locality Optimizing Triangle Counting Main Conference Mohsen Koohi Esfahani Queen's University Belfast, Peter Kilpatrick Queen's University Belfast, Hans Vandierendonck Queen's University Belfast Link to publication Pre-print | ||
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