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Counting Triangles in Large Graphs on GPU

Adam Polak
2016 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)  
The clustering coefficient and the transitivity ratio are concepts often used in network analysis, which creates a need for fast practical algorithms for counting triangles in large graphs.  ...  Previous research in this area focused on sequential algorithms, MapReduce parallelization, and fast approximations. In this paper we propose a parallel triangle counting algorithm for CUDA GPU.  ...  This creates a need for fast practical algorithms capable of counting triangles in large graphs.  ... 
doi:10.1109/ipdpsw.2016.108 dblp:conf/ipps/Polak16 fatcat:whypscdwdfdvdmq7pksfooicfm

A Comparative Study on Exact Triangle Counting Algorithms on the GPU

Leyuan Wang, Yangzihao Wang, Carl Yang, John D. Owens
2016 Proceedings of the ACM Workshop on High Performance Graph Processing - HPGP '16  
We implement exact triangle counting in graphs on the GPU using three different methodologies: subgraph matching to a triangle pattern; programmable graph analytics, with a set-intersection approach; and  ...  All three deliver best-of-class performance over CPU implementations and over comparable GPU implementations, with the graph-analytic approach achieving the best performance due to its ability to exploit  ...  Subgraph Matching-based TC Problem definition: finding all occurrences of a small query graph in a large data graph.  ... 
doi:10.1145/2915516.2915521 dblp:conf/hpdc/WangWYO16 fatcat:tqpllepszvgujet2mfgirfrfwa

TRUST: Triangle Counting Reloaded on GPUs

Santosh Pandey, Zhibin Wang, Sheng Zhong, Chen Tian, Bolong Zheng, Xiaoye Li, Lingda Li, Adolfy Hoisie, Caiwen Ding, Dong Li, Hang Liu
2021 IEEE Transactions on Parallel and Distributed Systems  
On the contrary, we advocate that i) hashing can help the key operations for scalable triangle counting on Graphics Processing Units (GPUs), i.e., list intersection and graph partitioning, ii)vertex-centric  ...  Triangle counting is a building block for a wide range of graph applications.  ...  This research is supported in part by the National Science Foundation CRII award No.  ... 
doi:10.1109/tpds.2021.3064892 fatcat:dzueeea3g5f47gty4vsbr7pkfi

On Analyzing Large Graphs Using GPUs

Amlan Chatterjee, Sridhar Radhakrishnan, John K. Antonio
2013 2013 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum  
Given a graph G = (V , E), we provide an algorithm to count the number of triangles in G, while storing the adjacency information on the global memory.  ...  In this paper, for storing large graphs, we propose and evaluate techniques to efficiently utilize the different levels of the memory hierarchy of GPUs, with the focus being on the larger global memory  ...  Counting triangles in large graphs is described in [1] . $ % & ? Fig. 2.  ... 
doi:10.1109/ipdpsw.2013.235 dblp:conf/ipps/ChatterjeeRA13 fatcat:sss4acsdbzet7ox7sm5btvvbfy

A Block-Based Triangle Counting Algorithm on Heterogeneous Environments [article]

Abdurrahman Yaşar and Sivasankaran Rajamanickam and Jonathan Berry and Ümit V. Çatalyürek
2020 arXiv   pre-print
Triangle counting is a fundamental building block in graph algorithms.  ...  The current state-of-the-art in triangle enumeration processes the Friendster graph in 2.1 seconds, not including data copy time between CPU and GPU.  ...  Acknowledgments: This work was supported in parts by the NSF grants CCF-1919021 and Sandia National Laboratories/Sandia Corp contract 2115504.  ... 
arXiv:2009.12457v1 fatcat:ttilsz73kjhq5l6afslof753eu

Fast BFS-Based Triangle Counting on GPUs [article]

Leyuan Wang, John D. Owens
2019 arXiv   pre-print
In this paper, we propose a novel method to compute triangle counting on GPUs.  ...  This work specifically focuses on leveraging our implementation on the triangle counting problem for the Subgraph Isomorphism Graph Challenge 2019, demonstrating a geometric mean speedup over the 2018  ...  We believe our optimizations are not limited to only triangle counting on the GPU.  ... 
arXiv:1909.02127v1 fatcat:idmflbcqhbe3pia7rp6kpsba6u

Fast BFS-Based Triangle Counting on GPUs

Leyuan Wang, John D. Owens
2019 2019 IEEE High Performance Extreme Computing Conference (HPEC)  
In this paper, we propose a novel method to compute triangle counting on GPUs.  ...  This work specifically focuses on leveraging our implementation on the triangle counting problem for the Subgraph Isomorphism Graph Challenge 2019, demonstrating a geometric mean speedup over the 2018  ...  This research was, in part, funded by the U.S. Government.  ... 
doi:10.1109/hpec.2019.8916434 dblp:conf/hpec/WangO19 fatcat:5t2ziaqdkrexbkouaaylozk4jy

Parallel triangle counting and k-truss identification using graph-centric methods

Chad Voegele, Yi-Shan Lu, Sreepathi Pai, Keshav Pingali
2017 2017 IEEE High Performance Extreme Computing Conference (HPEC)  
Abstract-We describe CPU and GPU implementations of parallel triangle-counting and k-truss identification in the Galois and IrGL systems.  ...  Both systems are based on a graph-centric abstraction called the operator formulation of algorithms.  ...  Triangles can be counted in a graph by using an overloaded matrix-matrix multiplication on adjacency and incidence matrices for the graph, as in miniTri [15] .  ... 
doi:10.1109/hpec.2017.8091037 dblp:conf/hpec/VoegeleLPP17 fatcat:4nkrif36xjaobajmkecsp2dq64

Efficient and Scalable Graph Pattern Mining on GPUs [article]

Xuhao Chen, Arvind
2022 arXiv   pre-print
Graph Pattern Mining (GPM) extracts higher-order information in a large graph by searching for small patterns of interest.  ...  Moreover, little work has been done on GPU to scale GPM computation to large problem sizes. We describe G2Miner, the first Graph Pattern Mining (GPM) framework that runs on multiple GPUs.  ...  Triangle counting on Tw4. Listing 4-cycle on Fr. 3-motif counting on Tw2.  ... 
arXiv:2112.09761v3 fatcat:pd3dklsyircptoj36vthvou22i

TriPoll: Computing Surveys of Triangles in Massive-Scale Temporal Graphs with Metadata [article]

Trevor Steil, Tahsin Reza, Keita Iwabuchi, Benjamin W. Priest, Geoffrey Sanders, Roger Pearce
2021 arXiv   pre-print
We contrast our approach with much of the prior effort on triangle analysis, which often focuses on simple triangle counting, usually in simple graphs with no metadata.  ...  demonstrate a triangle counting task on a 224 billion edge web graph in approximately half of the time of competing approaches, while additionally supporting metadata-aware capabilities.  ...  half the size of the 2012 graph), on an Nvidia DGX machine with eight GPUs (each with 32GB memory), it counted over four trillion triangles in under two minutes.  ... 
arXiv:2107.12330v1 fatcat:dgcjrwpz6jfixj4j24hg37ol3u

Fast Gunrock Subgraph Matching (GSM) on GPUs [article]

Leyuan Wang, John D. Owens
2020 arXiv   pre-print
In this paper, we propose a GPU-efficient subgraph isomorphism algorithm using the Gunrock graph analytic framework, GSM (Gunrock Subgraph Matching), to compute graph matching on GPUs.  ...  While most previous work on GPUs requires one-/two-step joining, we use one-step verification to decide the candidates in current frontier of nodes.  ...  A simpler but similar task to subgraph matching is triangle counting, which enumerates the triangles in a graph. Recent work from Wang et al.  ... 
arXiv:2003.01527v3 fatcat:5b4fw5qrb5gztnuknolttqrm2y

Hybrid CPU-GPU Framework for Network Motifs [article]

Ryan A. Rossi, Rong Zhou
2016 arXiv   pre-print
Both methods leverage GPU devices only, whereas the hybrid multi-core CPU-GPU framework leverages all available multi-core CPUs and multiple GPUs for computing graphlets in large networks.  ...  In addition to the hybrid multi-core CPU-GPU framework, we also investigate single GPU methods (using multiple cores) and multi-GPU methods that leverage all available GPUs simultaneously for computing  ...  Notice that for global macro counts, each GPU simply communicates the total number of triangles, cliques, and cycles, which can be easily aggregated locally (as opposed to the count of triangles, cliques  ... 
arXiv:1608.05138v2 fatcat:k64q5c5ajnfprfiuw3powmyu34

Parallel K-Clique Counting on GPUs [article]

Mohammad Almasri, Izzat El Hajj, Rakesh Nagi, Jinjun Xiong, Wen-mei Hwu
2022 arXiv   pre-print
While there has been work on related problems such as finding maximal cliques and generalized sub-graph matching on GPUs, k-clique counting in particular has yet to be explored in depth.  ...  However, there are no performant solutions for k-clique counting on GPUs.  ...  This work was supported in part by the IBM-Illinois Center for Cognitive Computing Systems Research (C3SR).  ... 
arXiv:2104.13209v2 fatcat:okpkslnp6bd2hnbuiwflnxv7ku

Pangolin: An Efficient and Flexible Graph Pattern Mining System on CPU and GPU [article]

Xuhao Chen, Roshan Dathathri, Gurbinder Gill, Keshav Pingali
2020 arXiv   pre-print
There is growing interest in graph pattern mining (GPM) problems such as motif counting.  ...  Acceleration on a V100 GPU further improves performance of Pangolin by 15x on average.  ...  GPM discovers relevant patterns in a given graph. One example is triangle counting, which is used to mine graphs in security applications [84] . .  ... 
arXiv:1911.06969v2 fatcat:jqj6f2gxxrcylfybqt4mkq6xsm

TCIM: Triangle Counting Acceleration With Processing-In-MRAM Architecture [article]

Xueyan Wang, Jianlei Yang, Yinglin Zhao, Yingjie Qi, Meichen Liu, Xingzhou Cheng, Xiaotao Jia, Xiaoming Chen, Gang Qu, Weisheng Zhao
2020 arXiv   pre-print
Triangle counting (TC) is a fundamental problem in graph analysis and has found numerous applications, which motivates many TC acceleration solutions in the traditional computing platforms like GPU and  ...  However, these approaches suffer from the bandwidth bottleneck because TC calculation involves a large amount of data transfers.  ...  Algorithm 1: TCIM: Triangle Counting with Processing-In-MRAM Architecture. : Graph G(V, E). Output: The number of triangles in G.  ... 
arXiv:2007.10702v1 fatcat:y37weagtq5bgvggp4jjaaphrtq
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