Application-driven graph partitioning
Graph partitioning is crucial to parallel computations on large graphs. The choice of partitioning strategies has strong impact on the performance of graph algorithms. For an algorithm of our interest, what partitioning strategy fits it the best and improves its parallel execution? Is it possible to...
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| Vydané v: | The VLDB journal Ročník 32; číslo 1; s. 149 - 172 |
|---|---|
| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.01.2023
Springer Nature B.V |
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| ISSN: | 1066-8888, 0949-877X |
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| Abstract | Graph partitioning is crucial to parallel computations on large graphs. The choice of partitioning strategies has strong impact on the performance of graph algorithms. For an algorithm of our interest, what partitioning strategy fits it the best and improves its parallel execution? Is it possible to provide a uniform partition to a batch of algorithms that run on the same graph simultaneously, and speed up each and every of them? This paper aims to answer these questions. We propose an application-driven hybrid partitioning strategy that, given a graph algorithm
A
, learns a cost model for
A
as polynomial regression. We develop partitioners that, given the learned cost model, refine an edge-cut or vertex-cut partition to a hybrid partition and reduce the parallel cost of
A
. Moreover, we extend the cost-driven strategy to support multiple algorithms at the same time and reduce the parallel cost of each of them. Using real-life and synthetic graphs, we experimentally verify that our partitioning strategy improves the performance of a variety of graph algorithms, up to
22.5
×
. |
|---|---|
| AbstractList | Graph partitioning is crucial to parallel computations on large graphs. The choice of partitioning strategies has strong impact on the performance of graph algorithms. For an algorithm of our interest, what partitioning strategy fits it the best and improves its parallel execution? Is it possible to provide a uniform partition to a batch of algorithms that run on the same graph simultaneously, and speed up each and every of them? This paper aims to answer these questions. We propose an application-driven hybrid partitioning strategy that, given a graph algorithm A, learns a cost model for A as polynomial regression. We develop partitioners that, given the learned cost model, refine an edge-cut or vertex-cut partition to a hybrid partition and reduce the parallel cost of A. Moreover, we extend the cost-driven strategy to support multiple algorithms at the same time and reduce the parallel cost of each of them. Using real-life and synthetic graphs, we experimentally verify that our partitioning strategy improves the performance of a variety of graph algorithms, up to 22.5×. Graph partitioning is crucial to parallel computations on large graphs. The choice of partitioning strategies has strong impact on the performance of graph algorithms. For an algorithm of our interest, what partitioning strategy fits it the best and improves its parallel execution? Is it possible to provide a uniform partition to a batch of algorithms that run on the same graph simultaneously, and speed up each and every of them? This paper aims to answer these questions. We propose an application-driven hybrid partitioning strategy that, given a graph algorithm A , learns a cost model for A as polynomial regression. We develop partitioners that, given the learned cost model, refine an edge-cut or vertex-cut partition to a hybrid partition and reduce the parallel cost of A . Moreover, we extend the cost-driven strategy to support multiple algorithms at the same time and reduce the parallel cost of each of them. Using real-life and synthetic graphs, we experimentally verify that our partitioning strategy improves the performance of a variety of graph algorithms, up to 22.5 × . |
| Author | Yu, Wenyuan Xu, Ruiqi Zhou, Jingren Fan, Wenfei Yin, Qiang |
| Author_xml | – sequence: 1 givenname: Wenfei surname: Fan fullname: Fan, Wenfei organization: University of Edinburgh, Shenzhen Institute of Computing Sciences, BDBC, Beihang University – sequence: 2 givenname: Ruiqi surname: Xu fullname: Xu, Ruiqi organization: University of Edinburgh – sequence: 3 givenname: Qiang orcidid: 0000-0003-3398-8345 surname: Yin fullname: Yin, Qiang email: q.yin@sjtu.edu.cn organization: Shanghai Jiao Tong University – sequence: 4 givenname: Wenyuan surname: Yu fullname: Yu, Wenyuan organization: Alibaba Group – sequence: 5 givenname: Jingren surname: Zhou fullname: Zhou, Jingren organization: Alibaba Group |
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| References_xml | – reference: ChandrashekarGSahinFA survey on feature selection methodsComput. Electr. Eng.2014401162810.1016/j.compeleceng.2013.11.024 – reference: Livejournal. http://snap.stanford.edu/data/soc-LiveJournal1.html (2009) – reference: NewmanMEWattsDJStrogatzSHRandom graph models of social networksProc. Natl. Acad. Sci.20029912566257210.1073/pnas.0125829991114.91362 – reference: KimMCandanKSSBV-Cut: vertex-cut based graph partitioning using structural balance verticesDKE20127228530310.1016/j.datak.2011.11.004 – reference: Karypis, G.: Metis and parmetis. In: Encyclopedia of Parallel Computing, pp. 1117–1124 (2011) – reference: Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. In: WWW, pp. 107–117 (1998) – reference: Bang-Jensen, J., Gutin, G.Z.: Digraphs: Theory, Algorithms and Applications. Springer (2008) – reference: ParkHStefanskiLRelative-error predictionStat. Probab. 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