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
The future of scientific workflows
Ewa Deelman,Tom Peterka,Ilkay Altintas,Christopher D. Carothers,Kerstin Kleese van Dam,Kenneth Moreland,Manish Parashar,Lavanya Ramakrishnan,Michela Taufer,Jeffrey S. Vetter +9 more
TL;DR: This work highlights use cases, computing systems, workflow needs, and concludes by summarizing the remaining challenges this community sees that inhibit large-scale scientific workflows from becoming a mainstream tool for extreme-scale science.
Proceedings ArticleDOI
NoDoze: Combatting Threat Alert Fatigue with Automated Provenance Triage
TL;DR: NODOZE generates alert dependency graphs that are two orders of magnitude smaller than those generated by traditional tools without sacrificing the vital information needed for the investigation, and decreases the volume of false alarms by 84%, saving analysts’ more than 90 hours of investigation time per week.
Journal ArticleDOI
Spinning fast iterative data flows
TL;DR: This work proposes a method to integrate incremental iterations, a form of workset iterations, with parallel dataflows and presents an extension to the programming model for incremental iterations that alleviates for the lack of mutable state in dataflow and allows for exploiting the sparse computational dependencies inherent in many iterative algorithms.
Journal ArticleDOI
Scaling queries over big RDF graphs with semantic hash partitioning
Kisung Lee,Ling Liu +1 more
TL;DR: A novel semantic hash partitioning approach is presented and a Semantic HAsh Partitioning-Enabled distributed RDF data management system is implemented, called Shape, which scales well and can process big RDF datasets more efficiently than existing approaches.
Proceedings ArticleDOI
GraphBIG: understanding graph computing in the context of industrial solutions
TL;DR: This paper characterized GraphBIG on real machines and observed extremely irregular memory patterns and significant diverse behavior across different computations, helping users understand the impact of modern graph computing on the hardware architecture and enables future architecture and system research.
References
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Journal ArticleDOI
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Journal Article
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