scholarly journals Improvement of the Efficiency of Genetic Algorithms for Scalable Parallel Graph Partitioning in a Multi-level Framework

2006 ◽  
pp. 243-252 ◽  
Author(s):  
Cédric Chevalier ◽  
François Pellegrini
Keyword(s):  
Genetic Algorithms ◽  
Graph Partitioning ◽  
Multi Level ◽  
Parallel Graph

Topology-Aware Load-Balance Schemes for Heterogeneous Graph Processing

2018 ◽  
pp. 113-143
Keyword(s):  
Parallel Processing ◽  
Complex Networks ◽  
Configuration Space ◽  
Real World ◽  
Graph Partitioning ◽  
Load Balance ◽  
Heterogeneous Computing ◽  
Graph Processing ◽  
Multi Level ◽  
Computing Approach

Inspired by the insights presented in Chapters 2, 3, and 4, in this chapter the authors present the KCMAX (K-Core MAX) and the KCML (K-Core Multi-Level) frameworks: novel k-core-based graph partitioning approaches that produce unbalanced partitions of complex networks that are suitable for heterogeneous parallel processing. Then they use KCMAX and KCML to explore the configuration space for accelerating BFSs on large complex networks in the context of TOTEM, a BSP heterogeneous GPU + CPU HPC platform. They study the feasibility of the heterogeneous computing approach by systematically studying different graph partitioning strategies, including the KCMAX and KCML algorithms, while processing synthetic and real-world complex networks.

scholarly journals Label Propagation-Based Parallel Graph Partitioning for Large-Scale Graph Data

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 72801-72813
Author(s):  
Minho Bae ◽  
Minjoong Jeong ◽  
Sangyoon Oh
Keyword(s):  
Graph Partitioning ◽  
Large Scale ◽  
Label Propagation ◽  
Graph Data ◽  
Parallel Graph
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