A survey on dynamic graph processing on GPUs: concepts, terminologies and systems

doi:10.1007/s11704-023-2656-1

Front. Comput. Sci.

2024, Vol. 18

Issue (4) : 184106 https://doi.org/10.1007/s11704-023-2656-1

Architecture

A survey on dynamic graph processing on GPUs: concepts, terminologies and systems

Hongru GAO^1,², Xiaofei LIAO¹, Zhiyuan SHAO^1,²(

), Kexin LI^1,², Jiajie CHEN¹, Hai JIN¹

¹. National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
². Zhejiang Lab, Hangzhou 311121, China

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Abstract

Graphs that are used to model real-world entities with vertices and relationships among entities with edges, have proven to be a powerful tool for describing real-world problems in applications. In most real-world scenarios, entities and their relationships are subject to constant changes. Graphs that record such changes are called dynamic graphs. In recent years, the widespread application scenarios of dynamic graphs have stimulated extensive research on dynamic graph processing systems that continuously ingest graph updates and produce up-to-date graph analytics results. As the scale of dynamic graphs becomes larger, higher performance requirements are demanded to dynamic graph processing systems. With the massive parallel processing power and high memory bandwidth, GPUs become mainstream vehicles to accelerate dynamic graph processing tasks. GPU-based dynamic graph processing systems mainly address two challenges: maintaining the graph data when updates occur (i.e., graph updating) and producing analytics results in time (i.e., graph computing). In this paper, we survey GPU-based dynamic graph processing systems and review their methods on addressing both graph updating and graph computing. To comprehensively discuss existing dynamic graph processing systems on GPUs, we first introduce the terminologies of dynamic graph processing and then develop a taxonomy to describe the methods employed for graph updating and graph computing. In addition, we discuss the challenges and future research directions of dynamic graph processing on GPUs.

Keywords dynamic graphs graph processing graph algorithms GPUs

Corresponding Author(s): Zhiyuan SHAO

Just Accepted Date: 09 March 2023 Issue Date: 24 May 2023

Cite this article:

Hongru GAO,Xiaofei LIAO,Zhiyuan SHAO, et al. A survey on dynamic graph processing on GPUs: concepts, terminologies and systems[J]. Front. Comput. Sci., 2024, 18(4): 184106.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-023-2656-1
https://academic.hep.com.cn/fcs/EN/Y2024/V18/I4/184106

Symbol	Description
$G = (V, E)$	A graph $G$ (directed or undirected), where $V$ is the vertex set and $E$ is the edge set.
$\| V \|$	The number of vertices in graph $G$ .
$\| E \|$	The number of edges in graph $G$ .
$N i n (u), N o u t (u)$	In-neighbor set and out-neighbor set of vertex $u$ in a directed graph.
$d i n (u), d o u t (u)$	In-degree and out-degree of vertex $u$ , denoting the number of in-neighbors and out-neighbors of $u$ , respectively.
$N (u)$	Neighbor set of vertex $u$ in an undirected graph.
$d (u)$	Degree of vertex $u$ , denoting the number of neighbors of $u$ .

Tab.1 Notations for graphs

Fig.1 Examples of undirected, directed, undirected weighted and directed weighted graphs (the number attached to an edge is the edge weight). (a) An example undirected graph; (b) an example directed graph; (c) an example undirected weighted graph; (d) an example directed weighted graph

Fig.2 Graph representations used to represent the example graph in Fig.1(d). (a) Adjacency matrix A =

(a i j)

; (b) coordinate list; (c) compressed sparse row (dotted arrows indicate the starting offsets of the out-neighbors of vertices); (d) adjacency list (solid arrows denote pointers to objects, and the notation “/” denotes pointers that do not refer to any object)

Fig.3 Updating the example graph in Fig.1(d) with a series of graph updates listed in chronological order

Tab.2 Comparisons between the stream graph model, the snapshot sequence model, and the temporal graph model

Fig.4 Schematic figures of the stream graph, snapshot sequence, temporal graph models under edge insertions (edges will not be removed once they are inserted), where

t 0

t 1

t 2

t 3

denote time points

Fig.5 General workflow of dynamic graph processing on GPUs

Fig.6 Schematic figures of graph updating methods (blue parts show the process of merging graph updates). (a) Array-based space management; (b) chain-based space management; (c) CSR-based space management

Fig.7 An example of incremental computing processes

Fig.8 A taxonomy of GPU-based dynamic graph processing systems

Tab.3 A classification of GPU-based dynamic graph updating systems

Fig.9 An example of the graph representation used in Hornet and the process of conducting edge insertions on Hornet. (a) An example graph representation of Hornet (solid arrows indicate pointers of vertices to their edges); (b) inserting three edges (0, 4), (1, 4), (1, 5) to the Hornet in Fig.9(a)

Fig.10 The process of conducting insertions on GPU-LSM (grey parts indicate that the levels are filled, while white parts indicate empty levels)

Fig.11 The graph representation of DCSR and edge insertions to DCSR. (a) An initial DCSR’s graph representation of the example graph in Fig.1(d) (the segments are all of length 3, separated by bold lines); (b) inserting two edges (0, 3, 7) and (0, 4, 5) to the DCSR in Fig.11(a) (modifications to the initial DCSR are highlighted with grey)

Fig.12 An example of the basic PMA structure and a graph representation combining PMA with CSR. (a) Inserting an entry 27 to the PMA structure (the interval values in a tree node are used to locate the corresponding segment in the array; the grey part shows the process of re-balancing the PMA); (b) a graph representation that combines PMA with CSR (diverse colors indicate the neighbors and sentinels of different vertices)

Fig.13 Data structures used in faimGraph, including vertex data blocks, pages, a vertex queue, a page queue, and a memory manager

Fig.14 An example of the graph representation in Slabhash (buckets and slabs are edge blocks of different capacities)

Tab.4 A classification of GPU-based dynamic graph computing systems

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