Proceedings ArticleDOI
Pregel: a system for large-scale graph processing
Grzegorz Malewicz,Matthew H. Austern,Aart J. C. Bik,James C. Dehnert,Ilan Horn,Naty Leiser,Grzegorz Czajkowski +6 more
- pp 135-146
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TLDR
A model for processing large graphs that has been designed for efficient, scalable and fault-tolerant implementation on clusters of thousands of commodity computers, and its implied synchronicity makes reasoning about programs easier.Abstract:
Many practical computing problems concern large graphs. Standard examples include the Web graph and various social networks. The scale of these graphs - in some cases billions of vertices, trillions of edges - poses challenges to their efficient processing. In this paper we present a computational model suitable for this task. Programs are expressed as a sequence of iterations, in each of which a vertex can receive messages sent in the previous iteration, send messages to other vertices, and modify its own state and that of its outgoing edges or mutate graph topology. This vertex-centric approach is flexible enough to express a broad set of algorithms. The model has been designed for efficient, scalable and fault-tolerant implementation on clusters of thousands of commodity computers, and its implied synchronicity makes reasoning about programs easier. Distribution-related details are hidden behind an abstract API. The result is a framework for processing large graphs that is expressive and easy to program.read more
Citations
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Proceedings ArticleDOI
Scalable Multi-threaded Community Detection in Social Networks
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Benchmarking Big Data Systems: A Review
TL;DR: This paper attempts to fill the void by presenting a review of the state-of-the-art big data benchmarking efforts by giving an overview of popular open-source benchmarks from the point of view of big data systems.
Proceedings ArticleDOI
Fast iterative graph computation: a path centric approach
TL;DR: The experimental results show that the path-centric approach outperforms vertex centric and edge-centric systems on a number of graph algorithms for both in-memory and out-of-core graphs.
Proceedings ArticleDOI
Making state explicit for imperative big data processing
TL;DR: The idea is to infer the dataflow and the types of state accesses from a Java program and use this information to generate a stateful dataflow graph (SDG), and it is shown that the performance of SDGs for several imperative online applications matches that of existing data-parallel processing frameworks.
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Iterative big data clustering algorithms: a review
TL;DR: It is believed that no well‐rounded review provides a significant comparison among parallel clustering algorithms using MapReduce, and this work aims to serve as a stepping stone for researchers who are studying big data clusteringgorithms.
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