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GitHub - jegonzal PowerGraph: PowerGraph: A framework for large-scale . . . GraphLab PowerGraph is a graph-based, high performance, distributed computation framework written in C++ The GraphLab PowerGraph academic project was started in 2009 at Carnegie Mellon University to develop a new parallel computation abstraction tailored to machine learning
PowerGraph: Distributed Graph-Parallel Computation on Natural . . . - USENIX PowerGraph abstraction we introduce a new approach to distributed graph placement and representation that exploits the structure of power-law graphs We provide a detailed analysis and experimental evaluation compar-ing PowerGraph to two popular graph-parallel systems Finally, we describe three different implementation strate-
PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs . . . Leveraging the PowerGraph abstraction we introduce a new approach to distributed graph placement and representation that exploits the structure of power-law graphs We provide a detailed analysis and experimental evaluation comparing PowerGraph to two popular graph-parallel systems
PowerGraph: DistributedGraph-ParallelComputationon NaturalGraphs PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs Authors: Joseph Gonzalez, Yucheng Low, et al Presenter: Ian Limarta Outline Setup GAS Abstraction Applications of PowerGraph PowerGraph Abstraction Experimental Results Setup: Why PowerGraph?
PowerGraph | Proceedings of the 10th USENIX conference on Operating . . . In this paper, we characterize the challenges of computation on natural graphs in the context of existing graph-parallel abstractions We then introduce the PowerGraph abstraction which exploits the internal structure of graph programs to address these challenges
PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs Authors: Joseph E Gonzalez, Yucheng Low, Haijie Gu, Danny Bickson, Carlos Guestrin Presented By: Rahul Yesantharao 03 31 2020 Paper Outline Presents the new PowerGraph framework for efficient Graph-Parallel processing
PowerGraph - Massachusetts Institute of Technology PowerGraph Distributed Graph-Parallel Computation on Natural Graphs Joseph E Gonzalez, Yucheng Low, Haijie Gu, Danny Bickson, Carlos Guestrin Presentation by: Richard Sollee Graph Abstractions Typical graph abstractions assume each vertex has few neighbors They partition the graph for parallel work based on vertices so they used edge cuts
PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs Distributed Graph Placement: Edge Cut Place a graph on p machines: construct a p-way edge-cut Overhead from every cut edge, and have to synchronize vertex and edge data across the cut Intuitively: evenly assign vertices to machines, allow edges to span machines
Papers - mwhittaker. github. io This paper presents a new graph processing abstraction (and implementation of the abstraction) called PowerGraph which works well on tame and natural graphs Graph-Parallel Abstractions In graph processing frameworks like Pregel and GraphLab, a graph is distributed across a cluster