GraphSteal: Dynamic Re-Partitioning for Efficient Graph Processing in Heterogeneous Clusters

Kumar, Dinesh; Raj, Arun; D, Janakiram

doi:10.1109/cloud.2017.63

Cited by 8 publications

(2 citation statements)

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“…DynamicDFEP [155], GrapH [156], and GraphSteal [157] are an example of dynamic edge partitioning. DynamicDFEP leverages Dfep [130] algorithm to make initial partitioning and introduces three update strategies, a complete partitioning method, partial partitioning method, and unit-based insertion.…”

Section: ) Dynamic Edge Partitioningmentioning

confidence: 99%

Graph Computing Systems and Partitioning Techniques: A Survey

et al. 2022

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Graphs are a tremendously suitable data representation that models the relationships of entities in many application domains, such as recommendation systems, machine learning, computational biology, social network analysis, and other application domains. Graphs with many vertices and edges have become quite prevalent in recent years. Therefore, graph computing systems with integrated various graph partitioning techniques have been envisioned as a promising paradigm to handle large-scale graph analytics in these application domains. However, scalable processing of large-scale graphs is challenging due to their high volume and inherent irregular structure of the real-world graphs. Hence, industry and academia have recently proposed graph partitioning and computing systems to efficiently process and analyze large-scale graphs. The graph partitioning and computing systems have been designed to improve scalability issues and reduce processing time complexity. This paper presents an overview, classification, and investigation of the most popular graph partitioning and computing systems. The various methods and approaches of graph partitioning and diverse categories of graph computing systems are presented. Finally, we discuss future challenges and research directions in graph partitioning and computing systems.

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Section: ) Dynamic Edge Partitioningmentioning

confidence: 99%

Graph Computing Systems and Partitioning Techniques: A Survey

et al. 2022

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“…Graphs have a lot of applications in information systems, in fact, there are very interesting works on graph theory such as Kumar, Raj, & Dharanipragada (2017), where heterogeneous graphs are analyzed to calculate using userdefined aggregate functions; or in the work of Ren, Schneider, Ovsjanikov, & Wonka (2017), that make groupings through graphs to design joint graphs to visualize segmented mesh collections; or Shi et al (2017) where to combine contextual social graphs.…”

Section: Introductionmentioning

confidence: 99%

Normalization by second order graphs: A visual alternative to simplify systems

Garro

2020

e-Ciencias de la Información

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This issue stems from the need for tools to analyze and make decisions around complex systems, where they apply the rules for linearly dependent sets, with the purpose of providing a visual tool, which serves to support complexity reduction processes. Two great precedents are Armstrong's Axioms, which has been applied from its publication to the present for database normalization, the other is set theory, a fundamental pillar of the Structured Query Language; based on them, together with the second-order logic, which adds qualifiers for subsets or properties, this work has been prepared, with an explanatory metrology with a qualitative approach, in an axiomatic system. As a result, a support tool has been provided to analyze complex systems naturally, by breaking cycles and detecting patterns, without interfering with existing models; however, for large systems it can be difficult to address it in its entirety, so it is recommended to divide by subsystems. With this work a technique has been accomplished, repeatable by anyone, but with a strong theoretical foundation. This work has great utility for the normalization of relational databases and an enormous potential for application in the design of systems beyond computational systems, it is also useful for understanding dependencies by their axiomatic nature.

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HaSGP: an effective graph partition method for heterogeneous-aware

Huang¹,

Zhou²

2022

Computing

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GraphSteal: Dynamic Re-Partitioning for Efficient Graph Processing in Heterogeneous Clusters

Cited by 8 publications

References 12 publications

Graph Computing Systems and Partitioning Techniques: A Survey

Graph Computing Systems and Partitioning Techniques: A Survey

Normalization by second order graphs: A visual alternative to simplify systems

HaSGP: an effective graph partition method for heterogeneous-aware

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