ABSTRACT
A graph application written using a distributed graph processing framework can perform over an order of magnitude slower than its high-performance, native counterpart. This issue stems from the aim, common to most graph frameworks, of restricting the scope of application development to specific graph constructs, such as, for example, vertex or edge programs.
In this paper we present Horizon, a distributed graph processing framework achieving close to native performance without penalizing productivity by providing a multi-layer, multi-abstraction model of computation. Compared to current frameworks, Horizon extends the scope of computation by exposing two notions usually relegated to implementations: graph data models and communication models. Horizon can reduce execution time by an average of 5.3× across different applications and datasets and process an order of magnitude larger graphs when compared to the state of the art.
- Scott Beamer, Krste Asanović, and David Patterson. 2012. Direction-optimizing Breadth-first Search. In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis (SC '12). IEEE Computer Society Press, Los Alamitos, CA, USA, Article 12, 10 pages. http://dl.acm.org/citation.cfm?id=2388996.2389013 Google ScholarDigital Library
- Aydin Buluc and John R Gilbert. 2011. The Combinatorial BLAS: Design, Implementation, and Applications. Int. J. High Perform. Comput. Appl. 25, 4 (Nov. 2011), 496--509. Google ScholarDigital Library
- Joseph E. Gonzalez, Reynold S. Xin, Ankur Dave, Daniel Crankshaw, Michael J. Franklin, and Ion Stoica. 2014. GraphX: Graph Processing in a Distributed Dataflow Framework. In 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI 14). USENIX Association, Broomfield, CO, 599--613. Google ScholarDigital Library
- 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 (SIGMOD '10). ACM, New York, NY, USA, 135--146. Google ScholarDigital Library
- Frank McSherry, Michael Isard, and Derek G. Murray. 2015. Scalability! But at what COST?. In 15th Workshop on Hot Topics in Operating Systems (HotOS XV). USENIX Association, Kartause Ittingen, Switzerland. https://www.usenix.org/conference/hotos15/workshop-program/presentation/mcsherry Google ScholarDigital Library
- Sujith Ravi. 2016. Graph-powered Machine Learning at Google. (2016). http://arxiv.org/abs/1107.0922Google Scholar
- Nadathur Satish, Narayanan Sundaram, Md. Mostofa Ali Patwary, Jiwon Seo, Jongsoo Park, M. Amber Hassaan, Shubho Sengupta, Zhaoming Yin, and Pradeep Dubey. 2014. Navigating the Maze of Graph Analytics Frameworks Using Massive Graph Datasets. In Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data (SIGMOD '14). ACM, New York, NY, USA, 979--990. Google ScholarDigital Library
- Narayanan Sundaram, Nadathur Satish, Md Mostofa Ali Patwary, Subramanya R. Dulloor, Michael J. Anderson, Satya Gautam Vadlamudi, Dipankar Das, and Pradeep Dubey. 2015. GraphMat: High Performance Graph Analytics Made Productive. Proc. VLDB Endow. 8, 11 (July 2015), 1214--1225. Google ScholarDigital Library
- Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Murphy McCauley, Michael J. Franklin, Scott Shenker, and Ion Stoica. 2012. Resilient Distributed Datasets: A Fault-tolerant Abstraction for In-memory Cluster Computing. In Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation (NSDI'12). USENIX Association, Berkeley, CA, USA, 2--2. http://dl.acm.org/citation.cfm?id=2228298.2228301 Google ScholarDigital Library
- Xiaowei Zhu, Wenguang Chen, Weimin Zheng, and Xiaosong Ma. 2016. Gemini: A Computation-Centric Distributed Graph Processing System. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). USENIX Association, GA, 301-316. https://www.usenix.org/conference/osdi16/technical-sessions/presentation/zhu Google ScholarDigital Library
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