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
Bipartite-oriented distributed graph partitioning for big learning
TL;DR: BiGraph leverages observations such as the skewed distribution of vertices, discriminated computation load and imbalanced data sizes between the two subsets of Vertices to derive a set of optimal graph partition algorithms that result in minimal vertex replication and network communication.
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PLAStiCC: predictive look-ahead scheduling for continuous dataflows on clouds
TL;DR: Plastic is proposed, an adaptive scheduling algorithm that balances resource cost and application throughput using a prediction-based look-ahead approach that not only addresses variations in the input data rates but also the underlying cloud infrastructure.
Proceedings ArticleDOI
Experimental Analysis of Streaming Algorithms for Graph Partitioning
Anil Pacaci,M. Tamer Özsu +1 more
TL;DR: The results show that the no partitioning algorithms performs best in all cases, and the choice of graph partitioning algorithm depends on: type and degree distribution of the graph, characteristics of the workloads, and specific application requirements.
Posted Content
Distributed-Memory Breadth-First Search on Massive Graphs
TL;DR: This chapter studies the problem of traversing large graphs using the breadth-first search order on distributed-memory supercomputers, and considers both the traditional level-synchronous top-down algorithm as well as the recently discovered direction optimizing algorithm.
Proceedings ArticleDOI
Zorro: zero-cost reactive failure recovery in distributed graph processing
TL;DR: This paper argues that distributed graph processing systems should instead use a reactive approach to failure recovery, and builds a system called Zorro that imbues this reactive approach, and integrates it into two graph processing system -- PowerGraph and LFGraph.
References
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