Network Performance Aware Graph Partitioning for Large Graph Processing Systems in the Cloud

2014 ◽  
pp. 244-269 ◽  
Keyword(s):  
Graph Partitioning ◽  
Network Performance ◽  
Graph Processing ◽  
Large 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 Cost-Aware Partitioning for Efficient Large Graph Processing in Geo-Distributed Datacenters

2020 ◽  
Vol 31 (7) ◽  
pp. 1707-1723
Author(s):  
Amelie Chi Zhou ◽  
Bingkun Shen ◽  
Yao Xiao ◽  
Shadi Ibrahim ◽  
Bingsheng He
Keyword(s):  
Graph Processing ◽  
Large Graph

scholarly journals An Association-Oriented Partitioning Approach for Streaming Graph Query

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Yun Hao ◽  
Gaofeng Li ◽  
Pingpeng Yuan ◽  
Hai Jin ◽  
Xiaofeng Ding
Keyword(s):  
Graph Partitioning ◽  
Sliding Window ◽  
Research Area ◽  
Graph Processing ◽  
Large Collection ◽  
Pagerank Algorithm ◽  
Graph Query ◽  
Partitioning Methods ◽  
Full Knowledge ◽  
Affinity Propagation Algorithm

The volumes of real-world graphs like knowledge graph are increasing rapidly, which makes streaming graph processing a hot research area. Processing graphs in streaming setting poses significant challenges from different perspectives, among which graph partitioning method plays a key role. Regarding graph query, a well-designed partitioning method is essential for achieving better performance. Existing offline graph partitioning methods often require full knowledge of the graph, which is not possible during streaming graph processing. In order to handle this problem, we propose an association-oriented streaming graph partitioning method named Assc. This approach first computes the rank values of vertices with a hybrid approximate PageRank algorithm. After splitting these vertices with an adapted variant affinity propagation algorithm, the process order on vertices in the sliding window can be determined. Finally, according to thelevelof these vertices and their association, the partition where the vertices should be distributed is decided. We compare its performance with a set of streaming graph partition methods and METIS, a widely adopted offline approach. The results show that our solution can partition graphs with hundreds of millions of vertices in streaming setting on a large collection of graph datasets and our approach outperforms other graph partitioning methods.

Improving large graph processing on partitioned graphs in the cloud

2012 ◽  
Author(s):  
Rishan Chen ◽  
Mao Yang ◽  
Xuetian Weng ◽  
Byron Choi ◽  
Bingsheng He ◽  
...  
Keyword(s):  
Graph Processing ◽  
Large Graph ◽  
Partitioned Graphs

Partitioning of Complex Networks for Heterogeneous Computing

2018 ◽  
pp. 88-112
Keyword(s):  
Community Detection ◽  
Graph Partitioning ◽  
Heterogeneous Computing ◽  
Fundamental Problem ◽  
Vital Role ◽  
Graph Processing ◽  
Graph Partitions ◽  
Hardware Architectures ◽  
Graph Computing ◽  
Parallel Graph

The most fundamental problem in BSP parallel graph computing is to decide how to partition and then distribute the graph among the available processors. In this regard, partitioning techniques for BSP heterogeneous computing should produce computing loads with different sizes (unbalanced partitions) in order to exploit processors with different computing capabilities. In this chapter, three major graph partitioning paradigms that are relevant to parallel graph processing are reviewed: balanced graph partitioning, unbalanced graph partitioning, and community detection. Then, the authors discuss how any of these paradigms fits the needs of graph heterogeneous computing where the suitability of partitions to hardware architectures plays a vital role. Finally, the authors discuss how the decomposition of networks in layers through the k-core decomposition provides the means for developing methods to produce unbalanced graph partitions that match multi-core and GPU processing capabilities.

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