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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Journal ArticleDOI
Scaling spark in the real world: performance and usability
Michael Armbrust,Tathagata Das,Aaron Davidson,Ali Ghodsi,Andrew Or,Josh Rosen,Ion Stoica,Patrick Wendell,Reynold Xin,Matei Zaharia +9 more
TL;DR: The main challenges and requirements that appeared in taking Spark to a wide set of users, and usability and performance improvements made to the engine in response are described.
Proceedings ArticleDOI
Improving large graph processing on partitioned graphs in the cloud
TL;DR: A novelgraph partitioning framework to improve the network performance of graph partitioning itself, partitioned graph storage and vertex-oriented graph processing, and the effectiveness of network performance aware optimizations on the large graph processing engine.
Journal ArticleDOI
A retrospective of knowledge graphs
TL;DR: This paper discusses in detail about the process of building knowledge graphs, and survey state-of-the-art techniques for automatic knowledge graph checking and expansion via logical inferring and reasoning.
Proceedings ArticleDOI
Theoretically Efficient Parallel Graph Algorithms Can Be Fast and Scalable
TL;DR: It is shown that theoretically-efficient parallel graph algorithms can scale to the largest publicly-available graphs using a single machine with a terabyte of RAM, processing them in minutes.
Journal ArticleDOI
Gunrock: GPU Graph Analytics
Yangzihao Wang,Yuechao Pan,Andrew Davidson,Yuduo Wu,Carl Yang,Leyuan Wang,Muhammad Osama,Chenshan Yuan,Weitang Liu,Andy Riffel,John D. Owens +10 more
TL;DR: The results show that on a single GPU, Gunrock has on average at least an order of magnitude speedup over Boost and PowerGraph, comparable performance to the fastest GPU hardwired primitives and CPU shared-memory graph libraries, and better performance than any other GPU high-level graph library.
References
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Journal ArticleDOI
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Jeffrey Dean,Sanjay Ghemawat +1 more
TL;DR: This paper presents the implementation of MapReduce, a programming model and an associated implementation for processing and generating large data sets that runs on a large cluster of commodity machines and is highly scalable.
Journal ArticleDOI
MapReduce: simplified data processing on large clusters
Jeffrey Dean,Sanjay Ghemawat +1 more
TL;DR: This presentation explains how the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks.
Journal ArticleDOI
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TL;DR: This paper provides an in-depth description of Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and looks at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.
Journal Article
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Sergey Brin,Lawrence Page +1 more
TL;DR: Google as discussed by the authors is a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems.