How Do Graph Relabeling Algorithms Improve Memory Locality?

Esfahani, Mohsen Koohi; Kilpatrick, Peter; Vandierendonck, Hans

doi:10.1109/ispass51385.2021.00023

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…The implications of real-world graphs on SpMV-based graph processing is studied in [28,29] by investigating the connection between different vertex classes of the graphs. It is also explained how the structure of a power-law graph provides better Push Locality (in traversing a graph in the push direction), or Pull Locality (for traversing a graph in the pull direction).…”

Section: Depth Of Components' Treesmentioning

confidence: 99%

Mastiff

Esfahani

Kilpatrick

Vandierendonck

2022

Proceedings of the 36th ACM International Conference on Supercomputing

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The Minimum Spanning Forest (MSF) problem finds usage in many different applications. While theoretical analysis shows that linear-time solutions exist, in practice, parallel MSF algorithms remain computationally demanding due to the continuously increasing size of data sets.In this paper, we study the MSF algorithm from the perspective of graph structure and investigate the implications of the power-law degree distribution of real-world graphs on this algorithm.We introduce the MASTIFF algorithm as a structureaware MSF algorithm that optimizes work efficiency by (1) dynamically tracking the largest forest component of each graph component and exempting them from processing, and (2) by avoiding topology-related operations such as relabeling and merging neighbour lists.The evaluations on 2 different processor architectures with up to 128 cores and on graphs of up to 124 billion edges, shows that Mastiff is 3.4-5.9× faster than previous works.

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Section: Depth Of Components' Treesmentioning

confidence: 99%

Mastiff

Esfahani

Kilpatrick

Vandierendonck

2022

Proceedings of the 36th ACM International Conference on Supercomputing

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“…Mohsen Koohi et al [23] study three reordering algorithms such as Slashburn [14], Gorder [16] and Rabbit order [15]. Their study suggests that memory locality improvement is related to the distribution of node degrees of the target graph.…”

Section: Related Workmentioning

confidence: 99%

“…Figures 21,22,23, 24 (gotten with Live Journal dataset) and figures 25, 26, 27, 28 (gotten with Orkut dataset) represent time reduction due to graph ordering heuristics (figures 21, 22, 25, 26) and degree-aware scheduling heuristics (figures 23, 24, 27, 28). We add at each figure a curve that shows the reduced time gotten with compression.…”

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confidence: 99%

Applying Data Structure Succinctness to Graph Numbering For Efficient Graph Analysis

Nguélé

Méhaut

2022

Revue Africaine De Recherche en Informatique Et Mathématiques Appliquées

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Graph algorithms have inherent characteristics, including data-driven computations and poor locality. These characteristics expose graph algorithms to several challenges, because most well studied (parallel) abstractions and implementation are not suitable for them. In our previous work [21, 22, 24], we show how to use some complex-network properties, including community structure and heterogeneity of node degree, to improve performance, by a proper memory management (Cn-order) and an appropriate thread scheduling (comm-deg-scheduling). In recent work [23], Besta et al. proposed log(graph), a graph representation that outperforms existing graph compression algorithms. In this paper, we show that our graph numbering heuristic and our scheduling heuristics can be improved when they are combined with log(graph) data structure. Experiments were made on multi-core machines. For example, on one node of a multi-core machine (Troll from Grid'5000), we showed that when combining our previously proposed heuristics with graph compression, with Pagerank being executing on Live Journal dataset, we can reduce with cn-order: cache-references from 29.94% (without compression) to 39.56% (with compression), cache-misses from 37.87% to 51.90% and hence time from 18.93% to 28.66%.

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Thrifty Label Propagation: Fast Connected Components for Skewed-Degree Graphs

Esfahani

Kilpatrick

Vandierendonck

2021

2021 IEEE International Conference on Cluster Computing (CLUSTER)

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Various concurrent algorithms have been proposed in the literature in recent years that mostly focus on the disjoint set approach to the Connected Components (CC) algorithm. However, these CC algorithms do not take the skewed structure of real-world graphs into account and as a result they do not benefit from common features of graph datasets to accelerate processing.We investigate the implications of the skewed degree distribution of real-world graphs on their connectivity and we use these features to introduce Thrifty Label Propagation as a structureaware CC algorithm obtained by incorporating 4 fundamental optimization techniques in the Label Propagation CC algorithm.Our evaluation on 15 real-world graphs and 2 different processor architectures shows that Thrifty accelerates the flow of labels and processes only 1.4% of the edges of the graph.In this way, Thrifty is up to 16× faster than state-of-the-art CC algorithms such as Afforest, Jayanti-Tarjan, and Breadth-First Search CC. In particular, Thrifty delivers 1.5 × −19.9× speedup for graph datasets larger than one billion edges.

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How Do Graph Relabeling Algorithms Improve Memory Locality?

Abstract: Relabeling algorithms aim to improve the poor memory locality of graph processing by changing the order of vertices. This paper analyses the functionality of three state-ofthe-art relabeling algorithms: SlashBurn, GOrder, and Rabbit-Order for real-world graphs.

Cited by 6 publications

References 15 publications

Mastiff

Mastiff

Applying Data Structure Succinctness to Graph Numbering For Efficient Graph Analysis

Thrifty Label Propagation: Fast Connected Components for Skewed-Degree Graphs

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