Hao Qi
ABSTRACT
Streaming graph has been broadly employed across various application domains. It involves updating edges to the graph and then performing analytics on the updated graph. However, existing solutions either suffer from poor data locality and high computation complexity for streaming graph analytics, or need high overhead to search and move graph data to ensure ordered neighbors during streaming graph update.
This paper presents a novel locality-centric streaming graph engine, called LSGraph, to enable efficient both graph analytics and graph update. The main novelty of this engine is a differentiated hierarchical indexed streaming graph representation approach to achieve efficient data search and movement for graph update and also maintain data locality and ordered neighbors for efficient graph analytics simultaneously. Besides, a locality-aware streaming graph data update mechanism is also proposed to efficiently regulate the distance of data movement, minimizing the overhead of memory access during graph update. We have implemented LSGraph and conducted a systematic evaluation on both real-world and synthetic datasets. Compared with three cutting-edge streaming graph engines, i.e., Terrace, Aspen, and PaC-tree, LSGraph achieves 2.98×-81.08×, 1.46×-12.56×, and 1.26×-10.31× speedups during graph update, while obtaining 1.02×-4.28×, 1.58×-3.55×, and 1.20×-2.72× speedups during graph analytics, respectively.
- Khaled Ammar, Frank McSherry, Semih Salihoglu, and Manas Joglekar. 2018. Distributed Evaluation of Subgraph Queries Using Worst-Case Optimal Low-Memory Dataflows. Proceedings of the VLDB Endowment 11, 6 (2018), 691--704.Google Scholar
Digital Library
- Muhammad A. Awad, Saman Ashkiani, Serban D. Porumbescu, and John D Owens. 2020. Dynamic Graphs on the GPU. In Proceedings of the 2020 IEEE International Parallel and Distributed Processing Symposium. 739--748.Google ScholarCross Ref
- Abanti Basak, Zheng Qu, Jilan Lin, Alaa R. Alameldeen, Zeshan Chishti, Yufei Ding, and Yuan Xie. 2021. Improving Streaming Graph Processing Performance Using Input Knowledge. In Proceedings of the 54th Annual IEEE/ACM International Symposium on Microarchitecture. 1036--1050.Google ScholarDigital Library
- Tal Ben-Nun, Maciej Besta, Simon Huber, Alexandros Nikolaos Ziogas, Daniel Peter, and Torsten Hoefler. 2019. A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learning. In Proceedings of the 2019 IEEE International Parallel and Distributed Processing Symposium. 66--77.Google ScholarCross Ref
- Michael A. Bender and Haodong Hu. 2007. An Adaptive Packed-Memory Array. ACM Transactions on Database Systems 32, 4 (2007), 26:1-26:43.Google ScholarDigital Library
- Maciej Besta, Marc Fischer, Vasiliki Kalavri, Michael Kapralov, and Torsten Hoefler. 2023. Practice of Streaming Processing of Dynamic Graphs: Concepts, Models, and Systems. IEEE Transactions on Parallel and Distributed Systems 34, 6 (2023), 1860--1876.Google ScholarDigital Library
- Maciej Besta, Raghavendra Kanakagiri, Grzegorz Kwasniewski, Rachata Ausavarungnirun, Jakub Beránek, Konstantinos Kanellopoulos, Kacper Janda, Zur Vonarburg-Shmaria, Lukas Gianinazzi, Ioana Stefan, Juan Gómez Luna, Jakub Golinowski, Marcin Copik, Lukas Kapp-Schwoerer, Salvatore Di Girolamo, Nils Blach, Marek Konieczny, Onur Mutlu, and Torsten Hoefler. 2021. SISA: Set-Centric Instruction Set Architecture for Graph Mining on Processing-in-Memory Systems. In Proceedings of the 54th Annual IEEE/ACM International Symposium on Microarchitecture. 282--297.Google ScholarDigital Library
- Laurent Bindschaedler, Jasmina Malicevic, Baptiste Lepers, Ashvin Goel, and Willy Zwaenepoel. 2021. Tesseract: Distributed, General Graph Pattern Mining on Evolving Graphs. In Proceedings of the 16th European Conference on Computer Systems. 458--473.Google ScholarDigital Library
- Mahdi Nazm Bojnordi and Farhan Nasrullah. 2019. ReTagger: An Efficient Controller for DRAM Cache Architectures. In Proceedings of the 56th Annual Design Automation Conference. 1--6.Google ScholarDigital Library
- Deepayan Chakrabarti, Yiping Zhan, and Christos Faloutsos. 2004. R-MAT: A Recursive Model for Graph Mining. In Proceedings of the 2004 SIAM International Conference on Data Mining. 442--446.Google ScholarCross Ref
- Qihang Chen, Boyu Tian, and Mingyu Gao. 2022. FINGERS: Exploiting Fine-Grained Parallelism in Graph Mining Accelerators. In Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems. 43--55.Google ScholarDigital Library
- Xuhao Chen and Arvind. 2022. Efficient and Scalable Graph Pattern Mining on GPUs. In Proceedings of the 16th USENIX Symposium on Operating Systems Design and Implementation. 857--877.Google Scholar
- Xuhao Chen, Tianhao Huang, Shuotao Xu, Thomas Bourgeat, Chanwoo Chung, and Arvind. 2021. FlexMiner: A Pattern-Aware Accelerator for Graph Pattern Mining. In Proceedings of the 48th Annual International Symposium on Computer Architecture. 581--594.Google ScholarDigital Library
- Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. 2009. Introduction to Algorithms. The MIT Press.Google Scholar
- Yifan Dai, Yien Xu, Aishwarya Ganesan, Ramnatthan Alagappan, Brian Kroth, Andrea Arpaci-Dusseau, and Remzi Arpaci-Dusseau. 2020. From WiscKey to Bourbon: A Learned Index for Log-Structured Merge Trees. In Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation. 155--171.Google Scholar
- Dean De Leo and Peter Boncz. 2021. Teseo and the Analysis of Structural Dynamic Graphs. Proceedings of the VLDB Endowment 14, 6 (2021), 1053--1066.Google ScholarDigital Library
- Laxman Dhulipala, Guy E. Blelloch, Yan Gu, and Yihan Sun. 2022. Pactrees: Supporting Parallel and Compressed Purely-functional Collections. In Proceedings of the 43rd ACM SIGPLAN International Conference on Programming Language Design and Implementation. 108--121.Google ScholarDigital Library
- Laxman Dhulipala, Guy E. Blelloch, and Julian Shun. 2019. Low-Latency Graph Streaming using Compressed Purely-Functional Trees. In Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation. 918--934.Google ScholarDigital Library
- Luisa Di Paola, Micol De Ruvo, Paola Paci, Daniele Santoni, and Alessandro Giuliani. 2013. Protein Contact Networks: An Emerging Paradigm in Chemistry. Chemical Reviews 113, 3 (2013), 1598--1613.Google ScholarCross Ref
- Jialin Ding, Umar Farooq Minhas, Jia Yu, Chi Wang, Jaeyoung Do, Yinan Li, Hantian Zhang, Badrish Chandramouli, Johannes Gehrke, Donald Kossmann, David Lomet, and Tim Kraska. 2020. ALEX: An Updatable Adaptive Learned Index. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. 969--984.Google ScholarDigital Library
- Jialin Ding, Vikram Nathan, Mohammad Alizadeh, and Tim Kraska. 2020. Tsunami: A Learned Multi-Dimensional Index for Correlated Data and Skewed Workloads. Proceedings of the VLDB Endowment 14, 2 (2020), 74--86.Google ScholarDigital Library
- Ayush Dubey, Greg D. Hill, Robert Escriva, and Emin Gün Sirer. 2016. Weaver: A High-Performance, Transactional Graph Database Based on Refinable Timestamps. Proceedings of the VLDB Endowment 9, 11 (2016), 852--863.Google ScholarDigital Library
- David Ediger, Rob McColl, Jason Riedy, and David A. Bader. 2012. STINGER: High Performance Data Structure for Streaming Graphs. In Proceedings of the 2012 IEEE Conference on High Performance Extreme Computing. 1--5.Google Scholar
- Priyank Faldu, Jeff Diamond, and Boris Grot. 2020. Domain-Specialized Cache Management for Graph Analytics. In Proceedings of the 2020 IEEE International Symposium on High Performance Computer Architecture. 234--248.Google ScholarCross Ref
- Guanyu Feng, Zixuan Ma, Daixuan Li, Shengqi Chen, Xiaowei Zhu, Wentao Han, and Wenguang Chen. 2021. RisGraph: A Real-Time Streaming System for Evolving Graphs to Support Sub-millisecond Per-update Analysis at Millions Ops/s. In Proceedings of the 2021 International Conference on Management of Data. 513--527.Google ScholarDigital Library
- Guoyao Feng, Xiao Meng, and Khaled Ammar. 2015. DISTINGER: A Distributed Graph Data Structure for Massive Dynamic Graph Processing. In Proceedings of the 2015 IEEE International Conference on Big Data. 1814--1822.Google ScholarDigital Library
- Paolo Ferragina and Giorgio Vinciguerra. 2020. The PGM-Index: A Fully-Dynamic Compressed Learned Index with Provable Worst-Case Bounds. Proceedings of the VLDB Endowment 13, 8 (2020), 1162--1175.Google ScholarDigital Library
- Per Fuchs, Domagoj Margan, and Jana Giceva. 2022. Sortledton: A Universal, Transactional Graph Data Structure. Proceedings of the VLDB Endowment 15, 6 (2022), 1173--1186.Google ScholarDigital Library
- Oded Green and David A. Bader. 2016. cuSTINGER: Supporting Dynamic Graph Aigorithms for GPUs. In Proceedings of the 2016 IEEE High Performance Extreme Computing Conference. 1--6.Google Scholar
- Benjamin Hilprecht, Andreas Schmidt, Moritz Kulessa, Alejandro Molina, Kristian Kersting, and Carsten Binnig. 2020. DeepDB: Learn from Data, Not from Queries! Proceedings of the VLDB Endowment 13, 7 (2020), 992--1005.Google Scholar
- Xiaolin Jiang, Chengshuo Xu, Xizhe Yin, Zhijia Zhao, and Rajiv Gupta. 2021. Tripoline: Generalized Incremental Graph Processing via Graph Triangle Inequality. In Proceedings of the 16th European Conference on Computer Systems. 17--32.Google ScholarDigital Library
- Anurag Khandelwal, Zongheng Yang, Evan Ye, Rachit Agarwal, and Ion Stoica. 2017. ZipG: A Memory-efficient Graph Store for Interactive Queries. In Proceedings of the 2017 ACM International Conference on Management of Data. 1149--1164.Google ScholarDigital Library
- Apostolos Kokolis, Dimitrios Skarlatos, and Josep Torrellas. 2019. Page-Seer: Using Page Walks to Trigger Page Swaps in Hybrid Memory Systems. In Proceedings of the 2019 IEEE International Symposium on High Performance Computer Architecture. 596--608.Google Scholar
- Tim Kraska, Alex Beutel, Ed H. Chi, Jeffrey Dean, and Neoklis Polyzotis. 2018. The Case for Learned Index Structures. In Proceedings of the 2018 International Conference on Management of Data. 489--504.Google ScholarDigital Library
- Pradeep Kumar and H. Howie Huang. 2016. G-Store: High-Performance Graph Store for Trillion-Edge Processing. In Proceedings of the 2016 International Conference for High Performance Computing, Networking, Storage and Analysis. 830--841.Google Scholar
- Pradeep Kumar and H. Howie Huang. 2019. GraphOne: A Data Store for Real-time Analytics on Evolving Graphs. In Proceedings of the 17th USENIX Conference on File and Storage Technologies. 249--263.Google ScholarDigital Library
- Jérôme Kunegis. 2013. KONECT: The Koblenz Network Collection. In Proceedings of the 22nd International Conference on World Wide Web. 1343--1350.Google ScholarDigital Library
- Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. 2010. What is Twitter, a Social Network or a News Media?. In Proceedings of the 19th International Conference on World Wide Web. 591--600.Google ScholarDigital Library
- Chris Lattner and Vikram Adve. 2004. LLVM: A Compilation Framework for Lifelong Program Analysis and Transformation. In Proceedings of the 2004 International Symposium on Code Generation and Optimization. 75--86.Google ScholarCross Ref
- Charles E. Leiserson. 2009. The Cilk++ Concurrency Platform. In Proceedings of the 46th Annual Design Automation Conference. 522--527.Google ScholarDigital Library
- Jure Leskovec and Andrej Krevl. 2014. SNAP Datasets: Stanford Large Network Dataset Collection. http://snap.stanford.edu/data.Google Scholar
- Pengfei Li, Yu Hua, Jingnan Jia, and Pengfei Zuo. 2021. FINEdex: A Fine-Grained Learned Index Scheme for Scalable and Concurrent Memory Systems. Proceedings of the VLDB Endowment 15, 2 (2021), 321--334.Google ScholarDigital Library
- Pengfei Li, Yu Hua, Pengfei Zuo, Zhangyu Chen, and Jiajie Sheng. 2023. ROLEX: A Scalable RDMA-oriented Learned Key-Value Store for Disaggregated Memory Systems. In Proceedings of the 21st USENIX Conference on File and Storage Technologies. 99--114.Google Scholar
- Pengfei Li, Hua Lu, Qian Zheng, Long Yang, and Gang Pan. 2020. LISA: A Learned Index Structure for Spatial Data. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. 2119--2133.Google ScholarDigital Library
- Kai Lu, Nannan Zhao, Jiguang Wan, Changhong Fei, Wei Zhao, and Tongliang Deng. 2022. TridentKV: A Read-Optimized LSM-Tree Based KV Store via Adaptive Indexing and Space-Efficient Partitioning. IEEE Transactions on Parallel and Distributed Systems 33, 8 (2022), 1953--1966.Google ScholarDigital Library
- Steffen Maass, Changwoo Min, Sanidhya Kashyap, Woonhak Kang, Mohan Kumar, and Taesoo Kim. 2017. MOSAIC: Processing a Trillion-Edge Graph on a Single Machine. In Proceedings of the 12th European Conference on Computer Systems. 527--543.Google ScholarDigital Library
- Peter Macko, Virendra J. Marathe, Daniel W. Margo, and Margo I. Seltzer. 2015. LLAMA: Efficient Graph Analytics Using Large Multiver-sioned Arrays. In Proceedings of the 2015 IEEE International Conference on Data Engineering. 363--374.Google Scholar
- Grzegorz Malewicz, Matthew H. Austern, Aart J.C Bik, James C. Dehnert, Ilan Horn, Naty Leiser, and Grzegorz Czajkowski. 2010. Pregel: A System for Large-Scale Graph Processing. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data. 135--146.Google ScholarDigital Library
- Mugilan Mariappan, Joanna Che, and Keval Vora. 2021. DZiG: Sparsity-Aware Incremental Processing of Streaming Graphs. In Proceedings of the 16th European Conference on Computer Systems. 83--98.Google ScholarDigital Library
- Mugilan Mariappan and Keval Vora. 2019. GraphBolt: Dependency-Driven Synchronous Processing of Streaming Graphs. In Proceedings of the 14th European Conference on Computer Systems. 1--16.Google ScholarDigital Library
- Richard C. Murphy, Kyle B. Wheeler, Brian W. Barrett, and James A. Ang. 2010. Introducing the Graph 500. Cray Users Group (CUG) 19 (2010), 45--74.Google Scholar
- Derek G. Murray, Frank McSherry, Michael Isard, Rebecca Isaacs, Paul Barham, and Martin Abadi. 2016. Incremental, Iterative Data Processing with Timely Dataflow. Commun. ACM 59, 10 (2016), 75--83.Google ScholarDigital Library
- Donald Nguyen, Andrew Lenharth, and Keshav Pingali. 2013. A Lightweight Infrastructure for Graph Analytics. In Proceedings of the 24th ACM Symposium on Operating Systems Principles. 456--471.Google ScholarDigital Library
- Prashant Pandey, Brian Wheatman, Helen Xu, and Aydin Buluc. 2021. Terrace: A Hierarchical Graph Container for Skewed Dynamic Graphs. In Proceedings of the 2021 International Conference on Management of Data. 1372--1385.Google ScholarDigital Library
- Hao Qi, Yu Zhang, Ligang He, Kang Luo, Jun Huang, Haoyu Lu, Jin Zhao, and Hai Jin. 2023. PSMiner: A Pattern-Aware Accelerator for High-Performance Streaming Graph Pattern Mining. In Proceedings of the 60th ACM/IEEE Design Automation Conference. 1--6.Google ScholarCross Ref
- Shafiur Rahman, Nael Abu-Ghazaleh, and Rajiv Gupta. 2020. Graph-Pulse: An Event-Driven Hardware Accelerator for Asynchronous Graph Processing. In Proceedings of the 53rd Annual IEEE/ACM International Symposium on Microarchitecture. 908--921.Google Scholar
- David Sayce. 2022. The Number of tweets per day in 2022. https://www.dsayce.com/social-media/tweets-day.Google Scholar
- Tao B. Schardl and I-Ting Angelina Lee. 2023. OpenCilk: A Modular and Extensible Software Infrastructure for Fast Task-Parallel Code. In Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming. 189--203.Google Scholar
- Tao B. Schardl, William S. Moses, and Charles E. Leiserson. 2019. Tapir: Embedding Recursive Fork-join Parallelism into LLVM's Intermediate Representation. ACM Transactions on Parallel Computing 6, 4 (2019), 1--33.Google ScholarDigital Library
- Mo Sha, Yuchen Li, Bingsheng He, and Kian-Lee Tan. 2017. Accelerating Dynamic Graph Analytics on GPUs. Proceedings of the VLDB Endowment 11, 1 (2017), 107--120.Google ScholarDigital Library
- Bin Shao, Haixun Wang, and Yatao Li. 2013. Trinity: A Distributed Graph Engine on a Memory Cloud. In Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data. 505--516.Google ScholarDigital Library
- Julian Shun and Guy E. Blelloch. 2013. Ligra: A Lightweight Graph Processing Framework for Shared Memory. In Proceedings of the 18th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. 135--146.Google Scholar
- Jinghan Sun, Shaobo Li, Yunxin Sun, Chao Sun, Dejan Vucinic, and Jian Huang. 2023. LeaFTL: A Learning-Based Flash Translation Layer for Solid-State Drives. In Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems. 442--456.Google ScholarDigital Library
- Zhaoyan Sun, Xuanhe Zhou, and Guoliang Li. 2023. Learned Index: A Comprehensive Experimental Evaluation. Proceedings of the VLDB Endowment 16, 8 (2023), 1992--2004.Google ScholarDigital Library
- Toyotaro Suzumura, Shunsuke Nishii, and Masaru Ganse. 2014. Towards Large-Scale Graph Stream Processing Platform. In Proceedings of the 23rd International Conference on World Wide Web. 1321--1326.Google ScholarDigital Library
- Chuzhe Tang, Youyun Wang, Zhiyuan Dong, Gansen Hu, Zhaoguo Wang, Minjie Wang, and Haibo Chen. 2020. XIndex: A Scalable Learned Index for Multicore Data Storage. In Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. 308--320.Google ScholarDigital Library
- Keval Vora, Rajiv Gupta, and Guoqing Xu. 2017. KickStarter: Fast and Accurate Computations on Streaming Graphs via Trimmed Approximations. In Proceedings of the 22nd International Conference on Architectural Support for Programming Languages and Operating Systems. 237--251.Google ScholarDigital Library
- Qinggang Wang, Long Zheng, Yu Huang, Pengcheng Yao, Chuangyi Gui, Xiaofei Liao, Hai Jin, Wenbin Jiang, and Fubing Mao. 2021. GraSU: A Fast Graph Update Library for FPGA-based Dynamic Graph Processing. In Proceedings of the 2021 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays. 149--159.Google ScholarDigital Library
- Xingda Wei, Rong Chen, and Haibo Chen. 2020. Fast RDMA-based Ordered Key-Value Store using Remote Learned Cache. In Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation. 117--135.Google Scholar
- Brian Wheatman and Helen Xu. 2018. Packed Compressed Sparse Row: A Dynamic Graph Representation. In Proceedings of the 2018 IEEE High Performance Extreme Computing Conference. 1--7.Google ScholarCross Ref
- Brian Wheatman and Helen Xu. 2021. A Parallel Packed Memory Array to Store Dynamic Graphs. In Proceedings of the 2021 Workshop on Algorithm Engineering and Experiments. 31--45.Google ScholarCross Ref
- Charith Wickramaarachchi, Alok Kumbhare, Marc Frincu, Charalampos Chelmis, and Viktor K. Prasanna. 2015. Real-time Analytics for Fast Evolving Social Graphs. In Proceedings of the 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. 829--834.Google Scholar
- Martin Winter, Daniel Mlakar, Rhaleb Zayer, Hans-Peter Seidel, and Markus Steinberger. 2018. faimGraph: High Performance Management of Fully-Dynamic Graphs Under Tight Memory Constraints on the GPU. In Proceedings of the 2018 International Conference for High Performance Computing, Networking, Storage and Analysis. 754--766.Google ScholarDigital Library
- Martin Winter, Rhaleb Zayer, and Markus Steinberger. 2017. Autonomous, Independent Management of Dynamic Graphs on GPUs. In Proceedings of the 2017 IEEE High Performance Extreme Computing Conference. 1--7.Google ScholarCross Ref
- Jiacheng Wu, Yong Zhang, Shimin Chen, Jin Wang, Yu Chen, and Chunxiao Xing. 2021. Updatable Learned Index with Precise Positions. Proceedings of the VLDB Endowment 14, 8 (2021), 1276--1288.Google ScholarDigital Library
- Shangyu Wu, Yufei Cui, Jinghuan Yu, Xuan Sun, Tei-Wei Kuo, and Chun Jason Xue. 2022. NFL: Robust Learned Index via Distribution Transformation. Proceedings of the VLDB Endowment 15, 10 (2022), 2188--2200.Google ScholarDigital Library
- Yanfeng Zhang, Qixin Gao, Lixin Gao, and Cuirong Wang. 2013. Maiter: An Asynchronous Graph Processing Framework for Delta-Based Accumulative Iterative Computation. IEEE Transactions on Parallel and Distributed Systems 25, 8 (2013), 2091--2100.Google ScholarCross Ref
- Yu Zhang, Xiaofei Liao, Lin Gu, Hai Jin, Kan Hu, Haikun Liu, and Bingsheng He. 2020. AsynGraph: Maximizing Data Parallelism for Efficient Iterative Graph Processing on GPUs. ACM Transactions on Architecture and Code Optimization 17, 4 (2020), 29:1-29:21.Google ScholarDigital Library
- Yu Zhang, Xiaofei Liao, Hai Jin, Ligang He, Bingsheng He, Haikun Liu, and Lin Gu. 2021. DepGraph: A Dependency-Driven Accelerator for Efficient Iterative Graph Processing. In Proceedings of the 2021 IEEE International Symposium on High-Performance Computer Architecture. 371--384.Google ScholarCross Ref
- Zhou Zhang, Zhaole Chu, Peiquan Jin, Yongping Luo, Xike Xie, Shouhong Wan, Yun Luo, Xufei Wu, Peng Zou, Chunyang Zheng, Guoan Wu, and Andy Rudoff. 2022. PLIN: A Persistent Learned Index for Non-Volatile Memory with High Performance and Instant Recovery. Proceedings of the VLDB Endowment 16, 2 (2022), 243--255.Google ScholarDigital Library
- Xiaowei Zhu, Guanyu Feng, Marco Serafini, Xiaosong Ma, Jiping Yu, Lei Xie, Ashraf Aboulnaga, and Wenguang Chen. 2020. LiveGraph: A Transactional Graph Storage System with Purely Sequential Adjacency List Scans. Proceedings of the VLDB Endowment 13, 7 (2020), 1020--1034.Google ScholarDigital Library
Index Terms
- LSGraph: A Locality-centric High-performance Streaming Graph Engine
Recommendations
Data Locality in Graph Engines: Implications and Preliminary Experimental Results
CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge ManagementThe size of graphs has dramatically increased. Graph engines for a single machine have been emerged to process these graphs efficiently. However, existing engines have overlooked a data locality which is an imperative factor to improve the performance ...
RealGraphGPU: A High-Performance GPU-Based Graph Engine toward Large-Scale Real-World Network Analysis
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge ManagementA graph, consisting of vertices and edges, has been widely adopted for network analysis. Recently, with the increasing size of real-world networks, many graph engines have been studied to efficiently process large-scale real-world graphs. RealGraph, one ...
RealGraph: A Graph Engine Leveraging the Power-Law Distribution of Real-World Graphs
WWW '19: The World Wide Web ConferenceAs the size of real-world graphs has drastically increased in recent years, a wide variety of graph engines have been developed to deal with such big graphs efficiently. However, the majority of graph engines have been designed without considering the ...
Comments