Pregel: a system for large-scale graph processing

By Grzegorz Malewicz, Matthew H. Austern, Aart J. C. Bik, James C. Dehnert, Ilan Horn, Naty Leiser, Grzegorz Czajkowski. SIGMOD Conference, 2010

4,148 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/2d867297dfe0d3ce2ed5b1d0f2dff88cac46ee94

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Citations in the corpus

Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center (2011, background): “Prominent examples include MapReduce [18], Dryad [24], MapReduce Online [17] (which supports streaming jobs), Pregel [28] (a specialized framework for graph computations), and others [27, 19, 30].”
Handbook of Big Data (2016, background): “In practice, particularly in large-p problems, there is rarely suﬃcient data to truly”
Foundations of Modern Query Languages for Graph Databases (2016, background): “It would also be interesting to understand how classical database-querying tasks compare to engines supporting the so-called vertex-centric programming model, such as Apache Giraph (Han and Daudjee 2015), GraphX (Xin et al. 2013), or Pregel (Malewicz et al. 2010).”
Graphicionado: A high-performance and energy-efficient accelerator for graph analytics (2016, background): “This renewed interest spurred the software community to develop more efficient graph analytics processing frameworks [4, 5, 23, 25, 33, 35, 46] as well as the hardware community to create hardware that can execute graph analytics applications with more efficiency than what the off-the-shelf, general”
From "Think Like a Vertex" to "Think Like a Graph" (2013, background): “As a result, a number of distributed/parallel graph processing systems have been proposed, including Pregel [15], its open source implementation Apache Giraph [1], GraphLab [14], Kineograph [6], Trinity [20], and Grace [23].”
GraphLab: A Distributed Framework for Machine Learning in the Cloud (2011, methodology): “Rather than adopting a message passing or data ﬂow model as in [8, 3], GraphLab allows the user deﬁned up-date functions complete freedom to read and modify any of the data on adjacent vertices and edges.”
BigDansing: A System for Big Data Cleansing (2015, methodology): “GraphX, in turn, uses the Bulk Synchronous Parallel (BSP) graph processing model [25] to process the hypergraph in parallel.”
GPS: a graph processing system (2013, background): “Google's proprietary Pregel system [26] was developed for this purpose.”
SNAP: A General Purpose Network Analysis and Graph Mining Library (2016, background): “In particular, research on graph processing in large-scale distributed environments [Gonzalez et al. 2012; Malewicz et al. 2010; Kang et al. 2009; Salihoglu and Widom 2013; Xin et al. 2013] provides efficient frameworks, but these frameworks implement only a handful of most common graph algorithms, ”
A scalable processing-in-memory accelerator for parallel graph processing (2015, methodology): “Our comprehensive evaluations using five state-of-the-art graph processing workloads with large real-world graphs show that the proposed architecture improves average system performance by a factor of ten and achieves 87% average energy reduction over conventional systems.”
Trends in big data analytics (2014, methodology): “Adopting this BSP model, Google proposed Pregel [63] for it-”
Efficient Subgraph Matching on Billion Node Graphs (2012, methodology): “Several distributed and parallel data processing systems, including Pregel [22] and InﬁniteGraph [3], have been proposed.”
The Stratosphere platform for big data analytics (2014, methodology): “The“BigData” software ecosystem includes distributed ﬁle systems [29], parallel data analysis platforms [8,12,15,22,44,70,74], data programming languages [13,19,37,57,69], and more specialized tools for speciﬁc data domains [53,54].”
Ray: A Distributed Framework for Emerging AI Applications (2017, background): “ to exploit—ever-growing quantities of data. This has led to the development of a plethora of frameworks for distributed data analysis, including batch [20, 64, 28], streaming [15, 39, 31], and graph [34, 35, 24] processing systems. The success of these frameworks has made it possible for organizati”
TicTac: Accelerating Distributed Deep Learning with Communication Scheduling (2018, background): “It is worth noting that deep learning systems with computational graphs are fundamentally different from graph processing systems (Malewicz et al., 2010; Hoque & Gupta, 2013; Xin et al., 2013).”
One Trillion Edges: Graph Processing at Facebook-Scale (2015, methodology): “Aggregators, as described in [33], provide eﬃcient shared state across workers.”
Connected Components at Scale via Local Contractions (2018, methodology): “The BSP paradigm is used by recent distributed graph processing systems like Pregel [MAB + 10] and Giraph [Fou16].”