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
Ligra: a lightweight graph processing framework for shared memory
Julian Shun,Guy E. Blelloch +1 more
TL;DR: This paper presents a lightweight graph processing framework that is specific for shared-memory parallel/multicore machines, which makes graph traversal algorithms easy to write and significantly more efficient than previously reported results using graph frameworks on machines with many more cores.
Proceedings ArticleDOI
To Push or To Pull: On Reducing Communication and Synchronization in Graph Computations
TL;DR: In this paper, the authors investigate the applicability of push-pull dichotomy to various algorithms and its impact on complexity, performance, and the amount of used locks, atomics, and reads/writes.
Proceedings ArticleDOI
Parallel Triangle Counting and Enumeration Using Matrix Algebra
TL;DR: This work presents a new primitive, masked matrix multiplication, that can be beneficial especially for the enumeration case and provides results from an initial implementation for the counting case along with various optimizations for communication reduction and load balance.
Proceedings ArticleDOI
Theoretically Efficient Parallel Graph Algorithms Can Be Fast and Scalable
TL;DR: It is shown that theoretically-efficient parallel graph algorithms can scale to the largest publicly-available graphs using a single machine with a terabyte of RAM, processing them in minutes.
Journal ArticleDOI
Gunrock: GPU Graph Analytics
Yangzihao Wang,Yuechao Pan,Andrew Davidson,Yuduo Wu,Carl Yang,Leyuan Wang,Muhammad Osama,Chenshan Yuan,Weitang Liu,Andy Riffel,John D. Owens +10 more
TL;DR: The results show that on a single GPU, Gunrock has on average at least an order of magnitude speedup over Boost and PowerGraph, comparable performance to the fastest GPU hardwired primitives and CPU shared-memory graph libraries, and better performance than any other GPU high-level graph library.
References
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Proceedings ArticleDOI
Low depth cache-oblivious algorithms
TL;DR: This paper describes several cache-oblivious algorithms with optimal work, polylogarithmic depth, and sequential cache complexities that match the best sequential algorithms, including the first such algorithms for sorting and for sparse-matrix vector multiply on matrices with good vertex separators.
Proceedings ArticleDOI
PATRIC: a parallel algorithm for counting triangles in massive networks
TL;DR: An efficient MPI-based distributed memory parallel algorithm, called PATRIC, for counting triangles in massive networks, which scales well to networks with billions of nodes and can compute the exact number of triangles in a network with one billion nodes and 10 billion edges in 16 minutes.
Posted Content
A space efficient streaming algorithm for triangle counting using the birthday paradox
TL;DR: In this paper, the authors proposed a space efficient algorithm that approximates the transitivity (global clustering coefficient) and total triangle count with only a single pass through a graph given as a stream of edges.
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Massive graph triangulation
TL;DR: A new algorithm is developed that is provably I/O and CPU efficient at the same time, without making any assumption on the input G at all, and outperformed the existing competitors by a factor over an order of magnitude in extensive experimentation.
Proceedings ArticleDOI
Triadic Measures on Graphs: The Power of Wedge Sampling
TL;DR: This work proposes a new method based on wedge sampling that allows for the fast and accurate approximation of all current variants of clustering coefficients and enables rapid uniform sampling of the triangles of a graph.