Proceedings ArticleDOI
Multicore triangle computations without tuning
Julian Shun,Kanat Tangwongsan +1 more
- pp 149-160
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TLDR
This paper describes the design and implementation of simple and fast multicore parallel algorithms for exact, as well as approximate, triangle counting and other triangle computations that scale to billions of nodes and edges, and is much faster than existing parallel approximate triangle counting implementations.Abstract:
Triangle counting and enumeration has emerged as a basic tool in large-scale network analysis, fueling the development of algorithms that scale to massive graphs. Most of the existing algorithms, however, are designed for the distributed-memory setting or the external-memory setting, and cannot take full advantage of a multicore machine, whose capacity has grown to accommodate even the largest of real-world graphs.read more
Citations
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Proceedings ArticleDOI
Exploiting CPU parallelism for triangle listing using hybrid summarized bit batch vector
TL;DR: This paper presents the new notions of summarized bit batch vector to represent the adjacency lists of massive graphs and proposes a parallel triangle listing algorithm that asynchronously access the indexed summarized data and join them in groups.
Proceedings ArticleDOI
LOTUS: locality optimizing triangle counting
TL;DR: The LOTUS algorithm is introduced as a structure-aware TC that optimizes locality and is evaluated 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--5.5X faster than previous works.
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$TC-Stream$TC-Stream: Large-Scale Graph Triangle Counting on a Single Machine Using GPUs
TL;DR: A high-performance graph processing system for a triangle counting algorithm on graph data with up to tens of billions of edges, which significantly exceeds the device memory capacity of Graphics Processing Units (GPUs).
Proceedings ArticleDOI
Accelerating Set Intersections over Graphs by Reducing-Merging
TL;DR: In this paper, the authors proposed a reducing-merging framework for set intersections over graphs rather than intersecting the two sets directly, where the vertices that cannot fall into the intersection are screened out by applying the range reduction.
Journal ArticleDOI
ProbGraph: High-Performance and High-Accuracy Graph Mining with Probabilistic Set Representations
Maciej Besta,Cesare Miglioli,Paolo Sylos Labini,Jakub Tvetek,Patrick Iff,Raghavendra Kanakagiri,Saleh Ashkboos,Kacper Janda,Michal Podstawski,Grzegorz Kwasniewski,Niels Gleinig,Flavio Vella,Onur Mutlu,Torsten Hoefler +13 more
TL;DR: ProbGraph is a graph representation that enables simple and fast approximate parallel graph mining with strong theoretical guarantees on work, depth, and result accuracy, and the key idea is to represent sets of vertices using probabilistic set representations such as Bloom filters.
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