Distributed Graph Computation Meets Machine Learning
TuX 2 is a new distributed graph engine that bridges graph computation and distributed machine learning. TuX 2 inherits the benefits of elegant graph computation model, efficient graph layout, and balanced parallelism to scale to billion-edge graphs, while extended and optimized for distributed mach...
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| Vydané v: | IEEE transactions on parallel and distributed systems Ročník 31; číslo 7; s. 1588 - 1604 |
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| Hlavní autori: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
IEEE
01.07.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1045-9219, 1558-2183 |
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| Abstract | TuX 2 is a new distributed graph engine that bridges graph computation and distributed machine learning. TuX 2 inherits the benefits of elegant graph computation model, efficient graph layout, and balanced parallelism to scale to billion-edge graphs, while extended and optimized for distributed machine learning to support heterogeneity in data model, Stale Synchronous Parallel in scheduling, and a new Mini-batch, Exchange, GlobalSync, and Apply ( MEGA ) model for programming. TuX 2 further introduces a hybrid vertex-cut graph optimization and supports various consistency models in fault tolerance for machine learning. We have developed a set of representative distributed machine learning algorithms in TuX 2 , covering both supervised and unsupervised learning. Compared to the implementations on distributed machine learning platforms, writing those algorithms in TuX 2 takes only about 25 percent of the code: our graph computation model hides the detailed management of data layout, partitioning, and parallelism from developers. The extensive evaluation of TuX 2 , using large datasets with up to 64 billion of edges, shows that TuX 2 outperforms PowerGraph/PowerLyra, the state-of-the-art distributed graph engines, by an order of magnitude, while beating two state-of-the-art distributed machine learning systems by at least 60 percent. |
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| AbstractList | TuX2 is a new distributed graph engine that bridges graph computation and distributed machine learning. TuX2 inherits the benefits of elegant graph computation model, efficient graph layout, and balanced parallelism to scale to billion-edge graphs, while extended and optimized for distributed machine learning to support heterogeneity in data model, Stale Synchronous Parallel in scheduling, and a new Mini-batch, Exchange, GlobalSync, and Apply ( MEGA ) model for programming. TuX2 further introduces a hybrid vertex-cut graph optimization and supports various consistency models in fault tolerance for machine learning. We have developed a set of representative distributed machine learning algorithms in TuX2 , covering both supervised and unsupervised learning. Compared to the implementations on distributed machine learning platforms, writing those algorithms in TuX2 takes only about 25 percent of the code: our graph computation model hides the detailed management of data layout, partitioning, and parallelism from developers. The extensive evaluation of TuX2 , using large datasets with up to 64 billion of edges, shows that TuX2 outperforms PowerGraph/PowerLyra, the state-of-the-art distributed graph engines, by an order of magnitude, while beating two state-of-the-art distributed machine learning systems by at least 60 percent. TuX 2 is a new distributed graph engine that bridges graph computation and distributed machine learning. TuX 2 inherits the benefits of elegant graph computation model, efficient graph layout, and balanced parallelism to scale to billion-edge graphs, while extended and optimized for distributed machine learning to support heterogeneity in data model, Stale Synchronous Parallel in scheduling, and a new Mini-batch, Exchange, GlobalSync, and Apply ( MEGA ) model for programming. TuX 2 further introduces a hybrid vertex-cut graph optimization and supports various consistency models in fault tolerance for machine learning. We have developed a set of representative distributed machine learning algorithms in TuX 2 , covering both supervised and unsupervised learning. Compared to the implementations on distributed machine learning platforms, writing those algorithms in TuX 2 takes only about 25 percent of the code: our graph computation model hides the detailed management of data layout, partitioning, and parallelism from developers. The extensive evaluation of TuX 2 , using large datasets with up to 64 billion of edges, shows that TuX 2 outperforms PowerGraph/PowerLyra, the state-of-the-art distributed graph engines, by an order of magnitude, while beating two state-of-the-art distributed machine learning systems by at least 60 percent. |
| Author | Xiao, Wencong Li, Zhen Zhou, Lidong Chen, Cheng Li, Wei Miao, Youshan Wu, Ming Xue, Jilong |
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| References | ref57 ref13 ma (ref80) 2018 ref56 ref59 ref15 ref14 ref52 ref54 kyrola (ref5) 2012 ref17 ref16 ho (ref18) 2013 power (ref83) 2010 de sa (ref30) 2015 xiao (ref1) 2017 ref51 ref50 lee (ref69) 2014 ref46 gonzalez (ref3) 2012 ref45 ref48 johnson (ref29) 2013 ref47 ref42 ref41 dean (ref68) 2012 zhu (ref55) 2016 ref49 ma (ref58) 2017 ref8 ref7 ref9 ref4 zhu (ref43) 2015 li (ref36) 2014 li (ref24) 2013 ref82 chilimbi (ref72) 2014 ref40 paszke (ref79) 2019 ref35 zhang (ref44) 2018 ref78 ref37 xiao (ref34) 2018 ref75 bennett (ref31) 2007 ref74 ref32 ref2 ref38 recht (ref28) 2011 gonzalez (ref39) 2016 nelson (ref12) 2015 ref71 prabhakaran (ref6) 2012 ref70 ref73 cui (ref19) 2014 dai (ref21) 2015 ref67 ref23 zhang (ref53) 2016 (ref81) 2012 ref26 ref25 ref64 ref20 gonzalez (ref10) 2014 abadi (ref76) 2016 ref63 ref66 ref22 chen (ref77) 2015 cui (ref33) 2014 ref27 ref60 zheng (ref11) 2015 ref62 ref61 mcsherry (ref65) 2015 |
| References_xml | – ident: ref78 doi: 10.1145/2647868.2654889 – start-page: 1223 year: 2012 ident: ref68 article-title: Large scale distributed deep networks publication-title: Proc Int Conf Neural Inf Process – start-page: 2674 year: 2015 ident: ref30 article-title: Taming the wild: A unified analysis of hogwild-style algorithms publication-title: Proc Int Conf Neural Inf Process – year: 2015 ident: ref65 article-title: Scalability! But at what cost? publication-title: Proc Workshop Hot Topics in Operating Systems – start-page: 37 year: 2014 ident: ref19 article-title: Exploiting bounded staleness to speed up big data analytics publication-title: Proc USENIX Annu Tech Conf – ident: ref71 doi: 10.1145/3037697.3037740 – start-page: 45 year: 2015 ident: ref11 article-title: FlashGraph: Processing billion-node graphs on an array of commodity SSDs publication-title: Proc USENIX Conf File Storage Technol – ident: ref46 doi: 10.1145/3064176.3064191 – year: 2007 ident: ref31 article-title: The Netflix Prize publication-title: Proc KDD Cup Workshop – start-page: 265 year: 2016 ident: ref76 article-title: TensorFlow: A system for large-scale machine learning publication-title: Proc 12th USENIX Symp Operating Syst Des Implementation – ident: ref66 doi: 10.14778/1920841.1920931 – ident: ref23 doi: 10.1145/1557019.1557121 – ident: ref50 doi: 10.1145/3322125 – ident: ref42 doi: 10.1109/IPDPS.2017.53 – start-page: 375 year: 2015 ident: ref43 article-title: GridGraph: Large-scale graph processing on a single machine using 2-level hierarchical partitioning publication-title: Proc USENIX Annu Tech Conf – ident: ref64 doi: 10.1145/2588555.2610518 – start-page: 79 year: 2015 ident: ref21 article-title: High-performance distributed ML at scale through parameter server consistency models publication-title: Proc 29th AAAI Conf Artif Intell – ident: ref17 doi: 10.14778/2212351.2212354 – ident: ref8 doi: 10.1145/2517349.2522740 – ident: ref67 doi: 10.1145/2124295.2124312 – start-page: 669 year: 2017 ident: ref1 article-title: Tux^{\mbox{2}}$2: Distributed graph computation for machine learning publication-title: Proc 10th USENIX Symp Netw Syst Des Implementation – ident: ref20 doi: 10.1145/2806777.2806778 – start-page: 441 year: 2018 ident: ref44 article-title: Cgraph: A correlations-aware approach for efficient concurrent iterative graph processing publication-title: Proc USENIX Annu Tech Conf – ident: ref54 doi: 10.1145/2442516.2442530 – start-page: 293 year: 2010 ident: ref83 article-title: Piccolo: Building fast, distributed programs with partitioned tables publication-title: Proc 9th USENIX Conf Operating Syst Des Implementation – ident: ref60 doi: 10.1145/3020078.3021739 – ident: ref70 doi: 10.1145/2901318.2901331 – ident: ref25 doi: 10.1109/TBDATA.2015.2472014 – ident: ref48 doi: 10.1109/IPDPS.2016.86 – ident: ref15 doi: 10.1145/2806777.2806849 – year: 2015 ident: ref77 article-title: MXNet: A flexible and efficient machine learning library for heterogeneous distributed systems – start-page: 2715 year: 2013 ident: ref29 article-title: Analyzing Hogwild parallel Gaussian Gibbs sampling publication-title: Proc Int Conf Neural Inf Process – ident: ref9 doi: 10.1145/2465351.2465369 – start-page: 285 year: 2016 ident: ref53 article-title: Exploring the hidden dimension in graph processing publication-title: Proc 12th USENIX Symp Operating Syst Des Implementation – year: 2013 ident: ref24 article-title: Distributed delayed proximal gradient methods publication-title: Proc NIPS Workshop Optim Mach Learn – ident: ref74 doi: 10.1109/TPDS.2017.2703904 – start-page: 17 year: 2012 ident: ref3 article-title: PowerGraph: Distributed graph-parallel computation on natural graphs publication-title: Proc 10th USENIX Conf Operat Syst Des Implementation – ident: ref38 doi: 10.1145/2517349.2522738 – ident: ref52 doi: 10.14778/3204028.3204035 – year: 2012 ident: ref6 article-title: Managing large graphs on multi-cores with graph awareness publication-title: Proc USENIX Annu Tech Conf – ident: ref47 doi: 10.14778/2809974.2809983 – ident: ref62 doi: 10.1109/SC.2014.50 – start-page: 195 year: 2017 ident: ref58 article-title: Garaph: Efficient GPU-accelerated graph processing on a single machine with balanced replication publication-title: Proc USENIX Conf USENIX Annu Tech Conf – start-page: 291 year: 2015 ident: ref12 article-title: Latency-tolerant software distributed shared memory publication-title: Proc USENIX Annu Tech Conf – ident: ref27 doi: 10.1145/2637166.2637236 – ident: ref45 doi: 10.1109/TPDS.2017.2776115 – ident: ref37 doi: 10.1561/2400000003 – ident: ref14 doi: 10.14778/2824032.2824077 – start-page: 599 year: 2014 ident: ref10 article-title: GraphX: Graph processing in a distributed dataflow framework publication-title: Proc 11th USENIX Symp Operating Syst Des Implementation – ident: ref41 doi: 10.1109/SC.2018.00059 – ident: ref59 doi: 10.1145/3192366.3192404 – ident: ref26 doi: 10.1145/2640087.2644155 – start-page: 19 year: 2014 ident: ref36 article-title: Communication efficient distributed machine learning with the parameter server publication-title: Proc Int Conf Neural Inf Process – ident: ref2 doi: 10.1145/1807167.1807184 – start-page: 571 year: 2014 ident: ref72 article-title: Project Adam: Building an efficient and scalable deep learning training system publication-title: Proc 11th USENIX Symp Operating Syst Des Implementation – ident: ref22 doi: 10.1145/2020408.2020426 – ident: ref35 doi: 10.1073/pnas.0307752101 – start-page: 301 year: 2016 ident: ref55 article-title: Gemini: A computation-centric distributed graph processing system publication-title: Proc 12th USENIX Symp Operating Syst Des Implementation – ident: ref82 doi: 10.1109/ICDM.2013.158 – start-page: 8024 year: 2019 ident: ref79 article-title: PyTorch: An imperative style, high-performance deep learning library publication-title: Adv Neural Inform Process Syst – start-page: 37 year: 2014 ident: ref33 article-title: Exploiting bounded staleness to speed up big data analytics publication-title: Proc USENIX Conf USENIX Annu Tech Conf – ident: ref63 doi: 10.1145/2807591.2807620 – year: 2018 ident: ref80 article-title: Towards efficient large-scale graph neural network computing publication-title: arXiv 1810 08403 – start-page: 2834 year: 2014 ident: ref69 article-title: On model parallelization and scheduling strategies for distributed machine learning publication-title: Proc Int Conf Neural Inf Process – year: 2012 ident: ref81 article-title: Mahout project – ident: ref16 doi: 10.1145/2815400.2815408 – ident: ref51 doi: 10.1109/HPEC.2018.8547538 – ident: ref73 doi: 10.1145/2901318.2901323 – ident: ref75 doi: 10.1016/j.jocs.2017.09.006 – ident: ref61 doi: 10.1109/ICDCS.2017.98 – start-page: 693 year: 2011 ident: ref28 article-title: HOGWILD: A lock-free approach to parallelizing stochastic gradient descent publication-title: Proc Int Conf Neural Inf Process – start-page: 31 year: 2012 ident: ref5 article-title: GraphChi: Large-scale graph computation on just a PC publication-title: Proc 10th USENIX Conf Operat Syst Des Implementation – ident: ref49 doi: 10.1177/1094342011403516 – start-page: 595 year: 2018 ident: ref34 article-title: Gandiva: Introspective cluster scheduling for deep learning publication-title: Proc 12th USENIX Symp Operating Syst Des Implementation – ident: ref57 doi: 10.1109/TKDE.2017.2745562 – ident: ref32 doi: 10.1145/2523616.2523633 – ident: ref7 doi: 10.1145/2517349.2522739 – year: 2016 ident: ref39 article-title: PowerGraph v2.2 – start-page: 1223 year: 2013 ident: ref18 article-title: More effective distributed ML via a stale synchronous parallel param eter server publication-title: Proc Int Conf Neural Inf Process – ident: ref56 doi: 10.1145/2694413.2694421 – ident: ref13 doi: 10.1145/2688500.2688507 – ident: ref40 doi: 10.1137/1.9781611972740.43 – ident: ref4 doi: 10.1145/2741948.2741970 |
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| SubjectTerms | Algorithms distributed machine learning Fault tolerance Graph computing Graph theory heterogeneity Layouts Machine learning MEGA model Optimization stale synchronous parallel |
| Title | Distributed Graph Computation Meets Machine Learning |
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