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52
PowerGraph: Distributed GraphParallel Computation on Natural Graphs
"... Largescale graphstructured computation is central to tasks ranging from targeted advertising to natural language processing and has led to the development of several graphparallel abstractions including Pregel and GraphLab. However, the natural graphs commonly found in the realworld have highly ..."
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Cited by 128 (4 self)
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Largescale graphstructured computation is central to tasks ranging from targeted advertising to natural language processing and has led to the development of several graphparallel abstractions including Pregel and GraphLab. However, the natural graphs commonly found in the realworld have highly skewed powerlaw degree distributions, which challenge the assumptions made by these abstractions, limiting performance and scalability. In this paper, we characterize the challenges of computation on natural graphs in the context of existing graphparallel abstractions. We then introduce the PowerGraph abstraction which exploits the internal structure of graph programs to address these challenges. Leveraging the PowerGraph abstraction we introduce a new approach to distributed graph placement and representation that exploits the structure of powerlaw graphs. We provide a detailed analysis and experimental evaluation comparing PowerGraph to two popular graphparallel systems. Finally, we describe three different implementation strategies for PowerGraph and discuss their relative merits with empirical evaluations on largescale realworld problems demonstrating order of magnitude gains. 1
GraphChi: Largescale Graph Computation On just a PC
 In Proceedings of the 10th USENIX conference on Operating Systems Design and Implementation, OSDI’12
, 2012
"... Current systems for graph computation require a distributed computing cluster to handle very large realworld problems, such as analysis on social networks or the web graph. While distributed computational resources have become more accessible, developing distributed graph algorithms still remains c ..."
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Cited by 115 (6 self)
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Current systems for graph computation require a distributed computing cluster to handle very large realworld problems, such as analysis on social networks or the web graph. While distributed computational resources have become more accessible, developing distributed graph algorithms still remains challenging, especially to nonexperts. In this work, we present GraphChi, a diskbased system for computing efficiently on graphs with billions of edges. By using a wellknown method to break large graphs into small parts, and a novel parallel sliding windows method, GraphChi is able to execute several advanced data mining, graph mining, and machine learning algorithms on very large graphs, using just a single consumerlevel computer. We further extend GraphChi to support graphs that evolve over time, and demonstrate that, on a single computer, GraphChi can process over one hundred thousand graph updates per second, while simultaneously performing computation. We show, through experiments and theoretical analysis, that GraphChi performs well on both SSDs and rotational hard drives. By repeating experiments reported for existing distributed systems, we show that, with only fraction of the resources, GraphChi can solve the same problems in very reasonable time. Our work makes largescale graph computation available to anyone with a modern PC. 1
GPS: A Graph Processing System
"... GPS (for Graph Processing System) is a complete opensource system we developed for scalable, faulttolerant, and easytoprogram execution of algorithms on extremely large graphs. GPS is similar to Google’s proprietary Pregel system [MAB+ 11], with some useful additional functionality described in ..."
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Cited by 68 (3 self)
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GPS (for Graph Processing System) is a complete opensource system we developed for scalable, faulttolerant, and easytoprogram execution of algorithms on extremely large graphs. GPS is similar to Google’s proprietary Pregel system [MAB+ 11], with some useful additional functionality described in the paper. In distributed graph processing systems like GPS and Pregel, graph partitioning is the problem of deciding which vertices of the graph are assigned to which compute nodes. In addition to presenting the GPS system itself, we describe how we have used GPS to study the effects of different graph partitioning schemes. We present our experiments on the performance of GPS under different static partitioning schemes—assigning vertices to workers “intelligently ” before the computation starts—and with GPS’s dynamic repartitioning feature, which reassigns vertices to different compute nodes during the computation by observing their message sending patterns.
Balanced Label Propagation for Partitioning Massive Graphs
, 2013
"... Partitioning graphs at scale is a key challenge for any application that involves distributing a graph across disks, machines, or data centers. Graph partitioning is a very well studied problem with a rich literature, but existing algorithms typically can not scale to billions of edges, or can not p ..."
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Cited by 25 (3 self)
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Partitioning graphs at scale is a key challenge for any application that involves distributing a graph across disks, machines, or data centers. Graph partitioning is a very well studied problem with a rich literature, but existing algorithms typically can not scale to billions of edges, or can not provide guarantees about partition sizes. In this work we introduce an efficient algorithm, balanced label propagation, for precisely partitioning massive graphs while greedily maximizing edge locality, the number of edges that are assigned to the same shard of a partition. By combining the computational efficiency of label propagation — where nodes are iteratively relabeled to the same ‘label ’ as the plurality of their graph neighbors — with the guarantees of constrained optimization — guiding the propagation by a linear program constraining the partition sizes — our algorithm makes it practically possible to partition graphs with billions of edges. Our algorithm is motivated by the challenge of performing graph predictions in a distributed system. Because this requires assigning each node in a graph to a physical machine with memory limitations, it is critically necessary to ensure the resulting partition shards do not overload any single machine. We evaluate our algorithm for its partitioning performance on the Facebook social graph, and also study its performance when partitioning Facebook’s ‘People You May Know ’ service (PYMK), the distributed system responsible for the feature extraction and ranking of the friendsoffriends of all active Facebook users. In a live deployment, we observed average query times and average network traffic levels that were 50.5 % and 37.1 % (respectively) when compared to the previous naive random sharding.
From "Think Like a Vertex " to "Think Like a Graph"
"... To meet the challenge of processing rapidly growing graph and network data created by modern applications, a number of distributed graph processing systems have emerged, such as Pregel and GraphLab. All these systems divide input graphs into partitions, and employ a “think like a vertex ” programmin ..."
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Cited by 25 (0 self)
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To meet the challenge of processing rapidly growing graph and network data created by modern applications, a number of distributed graph processing systems have emerged, such as Pregel and GraphLab. All these systems divide input graphs into partitions, and employ a “think like a vertex ” programming model to support iterative graph computation. This vertexcentric model is easy to program and has been proved useful for many graph algorithms. However, this model hides the partitioning information from the users, thus prevents many algorithmspecific optimizations. This often results in longer execution time due to excessive network messages (e.g. in Pregel) or heavy scheduling overhead to ensure data consistency (e.g. in GraphLab). To address this limitation, we propose a new “think like a graph ” programming paradigm. Under this graphcentric model, the partition structure is opened up to the users, and can be utilized so that communication within a partition can bypass the heavy message passing or scheduling machinery. We implemented this model in a new system, called Giraph++, based on Apache Giraph, an open source implementation of Pregel. We explore the applicability of the graphcentric model to three categories of graph algorithms, and demonstrate its flexibility and superior performance, especially on wellpartitioned data. For example, on a web graph with 118 million vertices and 855 million edges, the graphcentric version of connected component detection algorithm runs 63X faster and uses 204X fewer network messages than its vertexcentric counterpart. 1.
GraphX: Graph Processing in a Distributed Dataflow Framework
 USENIX ASSOCIATION 11TH USENIX SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION (OSDI ’14)
, 2014
"... In pursuit of graph processing performance, the systems community has largely abandoned generalpurpose distributed dataflow frameworks in favor of specialized graph processing systems that provide tailored programming abstractions and accelerate the execution of iterative graph algorithms. In thi ..."
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Cited by 23 (1 self)
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In pursuit of graph processing performance, the systems community has largely abandoned generalpurpose distributed dataflow frameworks in favor of specialized graph processing systems that provide tailored programming abstractions and accelerate the execution of iterative graph algorithms. In this paper we argue that many of the advantages of specialized graph processing systems can be recovered in a modern generalpurpose distributed dataflow system. We introduce GraphX, an embedded graph processing framework built on top of Apache Spark, a widely used distributed dataflow system. GraphX presents a familiar composable graph abstraction that is sufficient to express existing graph APIs, yet can be implemented using only a few basic dataflow operators (e.g., join, map, groupby). To achieve performance parity with specialized graph systems, GraphX recasts graphspecific optimizations as distributed join optimizations and materialized view maintenance. By leveraging advances in distributed dataflow frameworks, GraphX brings lowcost fault tolerance to graph processing. We evaluate GraphX on real workloads and demonstrate that GraphX achieves an order of magnitude performance gain over the base dataflow framework and matches the performance of specialized graph processing systems while enabling a wider range of computation.
FENNEL: Streaming Graph Partitioning for Massive Scale Graphs
"... Balanced graph partitioning in the streaming setting is a key problem to enable scalable and efficient computations on massive graph data such as web graphs, knowledge graphs, and graphs arising in the context of online social networks. Two families of heuristics for graph partitioning in the stream ..."
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Cited by 16 (1 self)
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Balanced graph partitioning in the streaming setting is a key problem to enable scalable and efficient computations on massive graph data such as web graphs, knowledge graphs, and graphs arising in the context of online social networks. Two families of heuristics for graph partitioning in the streaming setting are in wide use: place the newly arrived vertex in the cluster with the largest number of neighbors or in the cluster with the least number of nonneighbors. In this work, we introduce a framework which unifies the two seemingly orthogonal heuristics and allows us to quantify the interpolation between them. More generally, the framework enables a well principled design of scalable, streaming graph partitioning algorithms that are amenable to distributed implementations. We derive a novel onepass, streaming graph partitioning algorithm and show that it yields significant performance improvements over previous approaches using an extensive set of realworld and synthetic graphs. Surprisingly, despite the fact that our algorithm is a onepass streaming algorithm, we found its performance to be in many cases comparable to the defacto standard offline software METIS and in some cases even superiror. For instance, for the Twitter graph with more than 1.4 billion of edges, our method partitions the graph in about 40 minutes achieving a balanced partition that cuts as few as 6.8 % of edges, whereas it took more than 8 1 hours by METIS to 2 produce a balanced partition that cuts 11.98 % of edges. We also demonstrate the performance gains by using our graph partitioner while solving standard PageRank computation in a graph processing platform with respect to the communication cost and runtime.
Restreaming graph partitioning: Simple versatile algorithms for advanced balancing
 In ACM KDD
, 2013
"... Partitioning large graphs is difficult, especially when performed in the limited models of computation afforded to modern large scale computing systems. In this work we introduce restreaming graph partitioning and develop algorithms that scale similarly to streaming partitioning algorithms yet empi ..."
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Cited by 13 (1 self)
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Partitioning large graphs is difficult, especially when performed in the limited models of computation afforded to modern large scale computing systems. In this work we introduce restreaming graph partitioning and develop algorithms that scale similarly to streaming partitioning algorithms yet empirically perform as well as fully offline algorithms. In streaming partitioning, graphs are partitioned serially in a single pass. Restreaming partitioning is motivated by scenarios where approximately the same dataset is routinely streamed, making it possible to transform streaming partitioning algorithms into an iterative procedure. This combination of simplicity and powerful performance allows restreaming algorithms to be easily adapted to efficiently tackle more challenging partitioning objectives. In particular, we consider the problem of stratified graph partitioning, where each of many node attribute strata are balanced simultaneously. As such, stratified partitioning is well suited for the study of network effects on social networks, where it is desirable to isolate disjoint dense subgraphs with representative user demographics. To demonstrate, we partition a large social network such that each partition exhibits the same degree distribution in the original graph — a novel achievement for nonregular graphs. As part of our results, we also observe a fundamental difference in the ease with which social graphs are partitioned when compared to web graphs. Namely, the modular structure of web graphs appears to motivate full offline optimization, whereas the locally dense structure of social graphs precludes significant gains from global manipulations.
Fast Iterative Graph Computation with Block Updates
"... Scaling iterative graph processing applications to large graphs is an important problem. Performance is critical, as data scientists need to execute graph programs many times with varying parameters. The need for a highlevel, highperformance programming model has inspired much research on graph pr ..."
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Cited by 8 (1 self)
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Scaling iterative graph processing applications to large graphs is an important problem. Performance is critical, as data scientists need to execute graph programs many times with varying parameters. The need for a highlevel, highperformance programming model has inspired much research on graph programming frameworks. In this paper, we show that the important class of computationally light graph applications – applications that perform little computation per vertex – has severe scalability problems across multiple cores as these applications hit an early “memory wall ” that limits their speedup. We propose a novel blockoriented computation model, in which computation is iterated locally over blocks of highly connected nodes, significantly improving the amount of computation per cache miss. Following this model, we describe the design and implementation of a blockaware graph processing runtime that keeps the familiar vertexcentric programming paradigm while reaping the benefits of blockoriented execution. Our experiments show that blockoriented execution significantly improves the performance of our framework for several graph applications. 1.
Optimizations and Analysis of BSP Graph Processing Models on Public Clouds
"... Abstract — Largescale graph analytics is a central tool in many fields, and exemplifies the size and complexity of Big Data applications. Recent distributed graph processing frameworks utilize the venerable Bulk Synchronous Parallel (BSP) model and promise scalability for large graph analytics. Thi ..."
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Cited by 7 (3 self)
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Abstract — Largescale graph analytics is a central tool in many fields, and exemplifies the size and complexity of Big Data applications. Recent distributed graph processing frameworks utilize the venerable Bulk Synchronous Parallel (BSP) model and promise scalability for large graph analytics. This has been made popular by Google’s Pregel, which offers an architecture design for BSP graph processing. Public clouds offer democratized access to mediumsized compute infrastructure with the promise of rapid provisioning with no capital investment. Evaluating BSP graph frameworks on cloud platforms with their unique constraints is less explored. Here, we present optimizations and analysis for computationally complex graph analysis algorithms such as betweennesscentrality and allpairs shortest paths on a native BSP framework we have developed for the Microsoft Azure Cloud, modeled on the Pregel graph processing model. We propose novel heuristics for scheduling graph vertex processing in swaths to maximize resource utilization on cloud VMs that lead to a 3.5x performance improvement. We explore the effects of graph partitioning in the context of BSP, and show that even a well partitioned graph may not lead to performance improvement due to BSP's barrier synchronization. We end with a discussion on leveraging cloud elasticity for dynamically scaling the number of BSP workers to achieve a better performance than a static deployment, and at a significantly lower cost. Keywords Graph analytics; Cloud computing; Pregel;