GraphCT: Multithreaded Algorithms for Massive Graph Analysis

The digital world has given rise to massive quantities of data that include rich semantic and complex networks. A social graph, for example, containing hundreds of millions of actors and tens of billions of relationships is not uncommon. Analyzing these large data sets, even to answer simple analyti...

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Vydáno v:IEEE transactions on parallel and distributed systems Ročník 24; číslo 11; s. 2220 - 2229
Hlavní autoři: Ediger, David, Jiang, Karl, Riedy, E. Jason, Bader, David A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.11.2013
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithm design and analysis
Algorithms
Cray XMT
Data structures
Graph algorithms
Graphs
high-performance computing
Kernel
Libraries
Mathematical analysis
multithreaded architectures
Network analysis
Parallel processing
Platforms
Queries
Semantics
Servers
Studies
ISSN:1045-9219, 1558-2183
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Shrnutí:The digital world has given rise to massive quantities of data that include rich semantic and complex networks. A social graph, for example, containing hundreds of millions of actors and tens of billions of relationships is not uncommon. Analyzing these large data sets, even to answer simple analytic queries, often pushes the limits of algorithms and machine architectures. We present GraphCT, a scalable framework for graph analysis using parallel and multithreaded algorithms on shared memory platforms. Utilizing the unique characteristics of the Cray XMT, GraphCT enables fast network analysis at unprecedented scales on a variety of input data sets. On a synthetic power law graph with 2 billion vertices and 17 billion edges, we can find the connected components in 2 minutes. We can estimate the betweenness centrality of a similar graph with 537 million vertices and over 8 billion edges in under 1 hour. GraphCT is built for portability and performance.
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ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2012.323