From Think Parallel to Think Sequential

This paper presents GRAPE, a parallel GRAPh Engine for graph computations. GRAPE differs from previous graph systems in its ability to parallelize existing sequential graph algorithms as a whole, without the need for recasting the entire algorithms into a new model. Underlying GRAPE are a simple programming model, and a principled approach based on fixpoint computation with partial evaluation and incremental computation. Under a monotonic condition, GRAPE guarantees to converge at correct answers as long as the sequential algorithms are correct. We show how our familiar sequential graph algorithms can be parallelized by GRAPE. In addition to the ease of programming, we experimentally verify that GRAPE achieves comparable performance to the state-of-theart graph systems, using real-life and synthetic graphs.

[1]  Patrick Seemann,et al.  Matrix Factorization Techniques for Recommender Systems , 2014 .

[2]  Shirish Tatikonda,et al.  From "Think Like a Vertex" to "Think Like a Graph" , 2013, Proc. VLDB Endow..

[3]  Yuanyuan Tian,et al.  Big Graph Analytics Platforms , 2017, Found. Trends Databases.

[4]  Yinghui Wu,et al.  Parallelizing Sequential Graph Computations , 2017, SIGMOD Conference.

[5]  Johannes Gehrke,et al.  Asynchronous Large-Scale Graph Processing Made Easy , 2013, CIDR.

[6]  Umut A. Acar.  Self-adjusting computation: (an overview) , 2009, PEPM '09.

[7]  João Gama,et al.  Fast Incremental Matrix Factorization for Recommendation with Positive-Only Feedback , 2014, UMAP.

[8]  Thomas W. Reps,et al.  On the Computational Complexity of Dynamic Graph Problems , 1996, Theor. Comput. Sci..

[9]  Leslie G. Valiant,et al.  A bridging model for parallel computation , 1990, CACM.

[10]  Marc Lelarge,et al.  Balanced graph edge partition , 2014, KDD.

[11]  Konstantin Andreev,et al.  Balanced Graph Partitioning , 2004, SPAA '04.

[12]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[13]  Haixun Wang,et al.  Trinity: a distributed graph engine on a memory cloud , 2013, SIGMOD '13.

[14]  Michael Isard,et al.  Scalability! But at what COST? , 2015, HotOS.

[15]  Yinghui Wu,et al.  GRAPE: Parallelizing Sequential Graph Computations , 2017, Proc. VLDB Endow..

[16]  Joseph M. Hellerstein,et al.  Distributed GraphLab: A Framework for Machine Learning in the Cloud , 2012, Proc. VLDB Endow..

[17]  Carlos Guestrin,et al.  Distributed GraphLab : A Framework for Machine Learning and Data Mining in the Cloud , 2012 .

[18]  Wilfred Ng,et al.  Blogel: A Block-Centric Framework for Distributed Computation on Real-World Graphs , 2014, Proc. VLDB Endow..

[19]  Andy Schürr,et al.  Incremental Graph Pattern Matching , 2006 .

[20]  Thomas W. Reps,et al.  An Incremental Algorithm for a Generalization of the Shortest-Path Problem , 1996, J. Algorithms.

[21]  Reynold Xin,et al.  GraphX: Graph Processing in a Distributed Dataflow Framework , 2014, OSDI.

[22]  Sivasankaran Rajamanickam,et al.  Partitioning Trillion-Edge Graphs in Minutes , 2016, 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS).

[23]  Thomas A. Henzinger,et al.  Computing simulations on finite and infinite graphs , 1995, Proceedings of IEEE 36th Annual Foundations of Computer Science.

[24]  Robert Harper.  Self-adjusting computation , 2004, LICS 2004.

[25]  M. Tamer Özsu,et al.  An Experimental Comparison of Pregel-like Graph Processing Systems , 2014, Proc. VLDB Endow..

[26]  Aart J. C. Bik,et al.  Pregel: a system for large-scale graph processing , 2010, SIGMOD Conference.

[27]  Joseph Gonzalez,et al.  PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs , 2012, OSDI.

[28]  K. Selçuk Candan,et al.  SBV-Cut: Vertex-cut based graph partitioning using structural balance vertices , 2012, Data Knowl. Eng..

[29]  Robert E. Tarjan,et al.  Fibonacci heaps and their uses in improved network optimization algorithms , 1984, JACM.

[30]  Chao Tian,et al.  Incremental Graph Computations: Doable and Undoable , 2017, SIGMOD Conference.

[31]  Neil D. Jones,et al.  An introduction to partial evaluation , 1996, CSUR.

[32]  Leslie G. Valiant,et al.  General Purpose Parallel Architectures , 1991, Handbook of Theoretical Computer Science, Volume A: Algorithms and Complexity.