GraphChi: Large-Scale Graph Computation on Just a PC

By Aapo Kyrola, Guy E. Blelloch, Carlos Guestrin. USENIX Symposium on Operating Systems Design and Implementation, 2012

1,210 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b6d3df4881f8c211ac5a6449b1fd9bb26f93f76b

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Citations in the corpus

PowerLyra: Differentiated Graph Computation and Partitioning on Skewed Graphs (2019, methodology): “One machine of our six-node cluster (24 cores and 64GB DRAM) is used to run in-memory (Polymer [92] and Galois [53]) and out-of-core (X-Stream 22 [57] and GraphChi [39]) systems for both in-memory and out-of-core graphs using PageRank with 10 iterations.”
Gemini: A Computation-Centric Distributed Graph Processing System (2016, unknown): “Inspired by the implementation of several single-node graph processing systems [29, 42, 49, 57, 60], Gemini adopts a chunk-based partitioning scheme that enables a low-overhead scaling out design.”
PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs (2012, unknown): “Finally, [25] presents GraphChi: an efficient single-machine disk-based implementation of the GraphLab abstraction.”
Recurrent Recommender Networks (2017, methodology): “The implementation in GraphChi [17] is used in experiments.”
Fast Differentially Private Matrix Factorization (2015, methodology): “GraphChi Most of our experiments focus on a direct comparison to GraphChi [14].”
Scalability! But at what COST? (2015, unknown): “GraphChi: Large-Scale Graph Computation on Just a PC. OSDI 2012.”
SNAP: A General Purpose Network Analysis and Graph Mining Library (2016, background): “This increased importance of networks has sparked a growing interest in network analysis tools [Batagelj and Mrvar 1998; Hagberg et al. 2008; Kyrola et al. 2012; Malewicz et al. 2010].”
Scalable Recommendation with Hierarchical Poisson Factorization (2015, methodology): “We use the GraphLab implementation of CliMF [24] to scale to our large data sets, and use the default parameter settings in the package.”
Exploiting iterative-ness for parallel ML computations (2014, background): “Othermachine learning frameworks emphasize features outside the scope of this paper, such as out-of-core disk scheduling [27, 36].”
A Fast Parallel Stochastic Gradient Method for Matrix Factorization in Shared Memory Systems (2015, methodology): “Another parallel solver for matrix factorization is in GraphChi [Kyrola et al. 2012], which is a framework for graph computation.”
One Trillion Edges: Graph Processing at Facebook-Scale (2015, methodology): “Vertices Edges LiveJournal [9] 4.8M 69M Twitter 2010 [31] 42M 1.5B UK web graph 2007 [10] 109M 3.7B Yahoo web [8] 1.4B 6.6B Many specialized graph processing frameworks (e.g. [20, 21, 32, 44]) have been developed to run on web and social graphs such as those shown in Table 1.”
A lightweight infrastructure for graph analytics (2013, background): “GraphChi [17] is a shared-memory programming model for vertex programs that supports out-of-core processing when the input graph is too large to fit in memory.”
Power Iterated Color Refinement (2014, methodology): “Finally and probably most importantly, it shows that CR is easy to implement and readily scalable based on (sparse) matrix-matrix/vector multiplication, either by parallelization or just on a single PC or laptop using e.g. GraphChi (Kyrola, Blelloch, and Guestrin 2012).”
Trinity: a distributed graph engine on a memory cloud (2013, methodology): “To overcome the challenges, Pregel [28], GraphLab [1], PowerGraph [18], and GraphChi [25] adopt vertex centric graph computation models.”