GPS: a graph processing system

By Semih Salihoglu, Jennifer Widom. International Conference on Statistical and Scientific Database Management, 2013

588 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/f25e898c0d1b0a27a8f1567c96d8ba430f5e6a3f

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In Computer Networks and Communications

In Computer Vision and Pattern Recognition

In Information Systems

Citations in the corpus

PowerLyra: Differentiated Graph Computation and Partitioning on Skewed Graphs (2019, methodology): “Pregel and its open-source relatives (e.g., Giraph, 3 Hama, 4 and GPS [60]) use the Bulk Synchronous Parallel (BSP) model [74] with explicit messages to fetch all resources for the vertex computation.”
One Trillion Edges: Graph Processing at Facebook-Scale (2015, methodology): “Many other graph processing frameworks (e.g. [37, 15]) are also based on the BSP model.”
SNAP: A General Purpose Network Analysis and Graph Mining Library (2016, background): “ing systems fulﬁlls the requirements outlined above. 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 only i”
GraphChi: Large-Scale Graph Computation on Just a PC (2012, unknown): “Stanford GPS [37] is a new implementation of Pregel, with compelling performance.”
Mizan: a system for dynamic load balancing in large-scale graph processing (2013, methodology): “GPS: A Graph Processing System.”
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.”
Recent Advances in Graph Partitioning (2013, methodology): “GP is also used to partition graphs for parallel processing, for problems such as graph eigenvalue computations [25], breadthfirst search [31], triangle listing [43], PageRank and connected components [181].”
Ligra: a lightweight graph processing framework for shared memory (2013, background): “…Julian Shun Carnegie Mellon University jshun@cs.cmu.edu Abstract There has been signi.cant recent interest in parallel frameworks for processing graphs due to their applicability in studying social networks, the Web graph, networks in biology, and unstructured meshes in scienti.c simulation.”
Processing Data Where It Makes Sense: Enabling In-Memory Computation (2019, background): “A popular modern application is large-scale graph processing [87, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193].”
Gemini: A Computation-Centric Distributed Graph Processing System (2016, unknown): “Pull: Existing distributed graph processing systems either adopt a push-style [3, 26, 32, 43, 44] or a pull-style [11, 12, 16, 17, 23, 30] model, or provide both while used separately [13, 19, 22].”