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Distributed Graph Analytics

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Author : Unnikrishnan Cheramangalath
language : en
Publisher: Springer Nature
Release Date : 2020-04-17

Distributed Graph Analytics written by Unnikrishnan Cheramangalath and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-17 with Computers categories.

This book brings together two important trends: graph algorithms and high-performance computing. Efficient and scalable execution of graph processing applications in data or network analysis requires innovations at multiple levels: algorithms, associated data structures, their implementation and tuning to a particular hardware. Further, programming languages and the associated compilers play a crucial role when it comes to automating efficient code generation for various architectures. This book discusses the essentials of all these aspects. The book is divided into three parts: programming, languages, and their compilation. The first part examines the manual parallelization of graph algorithms, revealing various parallelization patterns encountered, especially when dealing with graphs. The second part uses these patterns to provide language constructs that allow a graph algorithm to be specified. Programmers can work with these language constructs without worrying about their implementation, which is the focus of the third part. Implementation is handled by a compiler, which can specialize code generation for a backend device. The book also includes suggestive results on different platforms, which illustrate and justify the theory and practice covered. Together, the three parts provide the essential ingredients for creating a high-performance graph application. The book ends with a section on future directions, which offers several pointers to promising topics for future research. This book is intended for new researchers as well as graduate and advanced undergraduate students. Most of the chapters can be read independently by those familiar with the basics of parallel programming and graph algorithms. However, to make the material more accessible, the book includes a brief background on elementary graph algorithms, parallel computing and GPUs. Moreover it presents a case study using Falcon, a domain-specific language for graph algorithms, to illustrate the concepts.

Distributed Graph Analytics

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Author : Unnikrishnan Cheramangalath
language : en
Publisher:
Release Date : 2020

Distributed Graph Analytics written by Unnikrishnan Cheramangalath and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Electronic books categories.

Distributed Graph Partitioning For Large Scale Graph Analytics

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Author : Lukas Rieger
language : en
Publisher:
Release Date : 2016

Distributed Graph Partitioning For Large Scale Graph Analytics written by Lukas Rieger and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.

Graphx Applied Building Scalable Real World Graph Analytics With Apache Spark

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Author : William E Clark
language : en
Publisher: Walzone Press
Release Date : 2025-11-26

Graphx Applied Building Scalable Real World Graph Analytics With Apache Spark written by William E Clark and has been published by Walzone Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-26 with Computers categories.

GraphX Applied: Building Scalable, Real-World Graph Analytics with Apache Spark is a practical, hands-on guide to designing and deploying high-performance graph solutions on Spark. Beginning with the motivations and architectural principles behind large-scale graph processing, the book explains how GraphX integrates with Spark’s distributed engine and shows how to model, construct, partition, and store graph data so raw records from disparate sources become efficient, queryable graph structures ready for analysis. At its core, the book provides a clear, example-driven treatment of GraphX APIs and transformations, walking readers through implementations of essential algorithms—PageRank, community detection, shortest paths, motif finding, and centrality metrics—adapted for distributed execution. It also distills production-grade best practices for optimization, fault tolerance, resource and cluster management, and workflow orchestration, equipping practitioners to build robust, scalable graph pipelines that perform reliably in real environments. Beyond fundamentals, GraphX Applied tackles advanced topics such as dynamic and temporal graph analytics, streaming computations, and the integration of graph neural networks, alongside security and operational considerations for distributed systems. Illustrated with real-world case studies from telecommunications, finance, cybersecurity, biomedicine, and social network analysis, the book concludes with a forward-looking perspective on the evolving landscape of distributed graph analytics and the GraphX community—an essential resource for data engineers, scientists, and architects.

Compiler And System For Resilient Distributed Heterogeneous Graph Analytics

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Author : Gurbinder Singh Gill
language : en
Publisher:
Release Date : 2020

Compiler And System For Resilient Distributed Heterogeneous Graph Analytics written by Gurbinder Singh Gill and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.

Graph analytics systems are used in a wide variety of applications including health care, electronic circuit design, machine learning, and cybersecurity. Graph analytics systems must handle very large graphs such as the Facebook friends graph, which has more than a billion nodes and 200 billion edges. Since machines have limited main memory, distributed-memory clusters with sufficient memory and computation power are required for processing of these graphs. In distributed graph analytics, the graph is partitioned among the machines in a cluster, and communication between partitions is implemented using a substrate like MPI. However, programming distributed-memory systems are not easy and the recent trend towards the processor heterogeneity has added to this complexity. To simplify the programming of graph applications on such platforms, this dissertation first presents a compiler called Abelian that translates shared-memory descriptions of graph algorithms written in the Galois programming model into efficient code for distributed-memory platforms with heterogeneous processors. An important runtime parameter to the compiler-generated distributed code is the partitioning policy. We present an experimental study of partitioning strategies for distributed work-efficient graph analytics applications on different CPU architecture clusters at large scale (up to 256 machines). Based on the study we present a simple rule of thumb to select among myriad policies. Another challenge of distributed graph analytics that we address in this dissertation is to deal with machine fail-stop failures, which is an important concern especially for long-running graph analytics applications on large clusters. We present a novel communication and synchronization substrate called Phoenix that leverages the algorithmic properties of graph analytics applications to recover from faults with zero overheads during fault-free execution and show that Phoenix is 24x faster than previous state-of-the-art systems. In this dissertation, we also look at the new opportunities for graph analytics on massive datasets brought by a new kind of byte-addressable memory technology with higher density and lower cost than DRAM such as intel Optane DC Persistent Memory. This enables the design of affordable systems that support up to 6TB of randomly accessible memory. In this dissertation, we present key runtime and algorithmic principles to consider when performing graph analytics on massive datasets on Optane DC Persistent Memory as well as highlight ideas that apply to graph analytics on all large-memory platforms. Finally, we show that our distributed graph analytics infrastructure can be used for a new domain of applications, in particular, embedding algorithms such as Word2Vec. Word2Vec trains the vector representations of words (also known as word embeddings) on large text corpus and resulting vector embeddings have been shown to capture semantic and syntactic relationships among words. Other examples include Node2Vec, Code2Vec, Sequence2Vec, etc (collectively known as Any2Vec) with a wide variety of uses. We formulate the training of such applications as a graph problem and present GraphAny2Vec, a distributed Any2Vec training framework that leverages the state-of-the-art distributed heterogeneous graph analytics infrastructure developed in this dissertation to scale Any2Vec training to large distributed clusters. GraphAny2Vec also demonstrates a novel way of combining model gradients during training, which allows it to scale without losing accuracy

Systems For Big Graph Analytics

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Author : Da Yan
language : en
Publisher: Springer
Release Date : 2017-05-31

Systems For Big Graph Analytics written by Da Yan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-31 with Computers categories.

There has been a surging interest in developing systems for analyzing big graphs generated by real applications, such as online social networks and knowledge graphs. This book aims to help readers get familiar with the computation models of various graph processing systems with minimal time investment. This book is organized into three parts, addressing three popular computation models for big graph analytics: think-like-a-vertex, think-likea- graph, and think-like-a-matrix. While vertex-centric systems have gained great popularity, the latter two models are currently being actively studied to solve graph problems that cannot be efficiently solved in vertex-centric model, and are the promising next-generation models for big graph analytics. For each part, the authors introduce the state-of-the-art systems, emphasizing on both their technical novelties and hands-on experiences of using them. The systems introduced include Giraph, Pregel+, Blogel, GraphLab, CraphChi, X-Stream, Quegel, SystemML, etc. Readers will learn how to design graph algorithms in various graph analytics systems, and how to choose the most appropriate system for a particular application at hand. The target audience for this book include beginners who are interested in using a big graph analytics system, and students, researchers and practitioners who would like to build their own graph analytics systems with new features.

Large Scale Graph Analysis System Algorithm And Optimization

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Author : Yingxia Shao
language : en
Publisher: Springer Nature
Release Date : 2020-07-01

Large Scale Graph Analysis System Algorithm And Optimization written by Yingxia Shao and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-01 with Computers categories.

This book introduces readers to a workload-aware methodology for large-scale graph algorithm optimization in graph-computing systems, and proposes several optimization techniques that can enable these systems to handle advanced graph algorithms efficiently. More concretely, it proposes a workload-aware cost model to guide the development of high-performance algorithms. On the basis of the cost model, the book subsequently presents a system-level optimization resulting in a partition-aware graph-computing engine, PAGE. In addition, it presents three efficient and scalable advanced graph algorithms – the subgraph enumeration, cohesive subgraph detection, and graph extraction algorithms. This book offers a valuable reference guide for junior researchers, covering the latest advances in large-scale graph analysis; and for senior researchers, sharing state-of-the-art solutions based on advanced graph algorithms. In addition, all readers will find a workload-aware methodology for designing efficient large-scale graph algorithms.

Big Graph Analytics On Just A Single Pc

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Author : Kai Wang
language : en
Publisher:
Release Date : 2019

Big Graph Analytics On Just A Single Pc written by Kai Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.

As graph data becomes ubiquitous in modern computing, developing systems to efficiently process large graphs has gained increasing popularity. There are two major types of analytical problems over large graphs: graph computation and graph mining. Graph computation includes a set of problems that can be represented through liner algebra over an adjacency matrix based representation of the graph. Graph mining aims to discover complex structural patterns of a graph, for example, finding relationship patterns in social media network, detecting link spam in web data. Due to their importance in machine learning, web application and social media, graph analytical problems have been extensively studied in the past decade. Practical solutions have been implemented in a wide variety of graph analytical systems. However, most of the existing systems for graph analytics are distributed frameworks, which suffer from one or more of the following drawbacks: (1) many of the (current and future) users performing graph analytics will be domain experts with limited computer science background. They are faced with the challenge of managing a cluster, which involves tasks such as data partitioning and fault tolerance they are not familiar with; (2) not all users have access to enterprise cluster in their daily development tasks; (3) distributed graph systems commonly suffer from large startup and communication overhead; and (4) load balancing in a distributed system is another major challenge. Some graph algorithms have dynamic working sets and and it is thus hard to distribute the workload appropriately before the execution. In this dissertation, we identify three categories of graph workloads for which single-machine systems are more suitable than distributed systems: (1) analytical queries that do not need exact answers; (2) program analysis tasks that are widely used to find bugs in real-world software; and (3) graph mining algorithms that are important for many information-retrieval tasks. Based on these observations, we have developed a set of single-machine graph systems to deliver efficiency and scalability specifically for these workloads. In particular, this dissertation makes the following contributions. The first contribution is the design and implementation of a single-machine graph query system named GraphQ, which divides a large graph into partitions and merges them with the guidance from an abstraction graph. By using multiple levels of abstraction, it can quickly rule out infeasible solutions and identify mergeable partitions. GraphQ uses the memory capacity as a budget and tries its best to find solutions before exhausting the memory, making it possible to answer analytical queries over very large graphs with resources affordable to a single PC. The second contribution is the design and implementation of Graspan, a single-machine, disk-based graph processing system tailored for interprocedural static analyses. Given a program graph and a grammar specification of an analysis, Graspan uses an edge-pair centric computation model to compute dynamic transitive closures on very large program graphs. With the help of novel graph processing techniques, we turn sophisticated code analyses into scalable Big Graph analytics. The third contribution of this dissertation is a single-machine, out-of-core graph mining system, called RStream, which leverages disk support to support efficient edge streaming for mining very large graphs. RStream employs a rich programming model that exposes relational algebra for developers to express a wide variety of mining tasks and implements a runtime engine that delivers efficiency with tuple streaming. In conclusion, this dissertation attempts to explore the opportunities of building single-machine graph systems for scenarios where distributed systems do not work well. Our experimental results demonstrate that the techniques proposed in this dissertation can efficiently solve big graph analytical problems on a single consumer PC. We hope that these promising results will encourage future work to continue building affordable single-machine systems for a rich set of datasets and analytical tasks.

Practical Graph Analytics With Apache Giraph

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Author : Roman Shaposhnik
language : en
Publisher: Apress
Release Date : 2015-11-19

Practical Graph Analytics With Apache Giraph written by Roman Shaposhnik and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-19 with Computers categories.

Practical Graph Analytics with Apache Giraph helps you build data mining and machine learning applications using the Apache Foundation’s Giraph framework for graph processing. This is the same framework as used by Facebook, Google, and other social media analytics operations to derive business value from vast amounts of interconnected data points. Graphs arise in a wealth of data scenarios and describe the connections that are naturally formed in both digital and real worlds. Examples of such connections abound in online social networks such as Facebook and Twitter, among users who rate movies from services like Netflix and Amazon Prime, and are useful even in the context of biological networks for scientific research. Whether in the context of business or science, viewing data as connected adds value by increasing the amount of information available to be drawn from that data and put to use in generating new revenue or scientific opportunities. Apache Giraph offers a simple yet flexible programming model targeted to graph algorithms and designed to scale easily to accommodate massive amounts of data. Originally developed at Yahoo!, Giraph is now a top top-level project at the Apache Foundation, and it enlists contributors from companies such as Facebook, LinkedIn, and Twitter. Practical Graph Analytics with Apache Giraph brings the power of Apache Giraph to you, showing how to harness the power of graph processing for your own data by building sophisticated graph analytics applications using the very same framework that is relied upon by some of the largest players in the industry today.

Improving Distributed Graph Processing By Load Balancing And Redundancy Reduction

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Author : Shuang Song (Ph. D.)
language : en
Publisher:
Release Date : 2020

Improving Distributed Graph Processing By Load Balancing And Redundancy Reduction written by Shuang Song (Ph. D.) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.

The amount of data generated every day is growing exponentially in the big data era. A significant portion of this data is stored as graphs in various domains, such as online retail and social networks. Analyzing large-scale graphs provides important insights that are highly utilized in many areas, such as recommendation systems, banking systems, and medical diagnosis. To accommodate analysis on large-scale graphs, developers from industry and academia design the distributed graph processing systems. However, processing graphs in a distributed manner suffers performance inefficiencies caused by workload imbalance and redundant computations. For instance, while data centers are trending towards a large amount of heterogeneous processing machines, graph partitioners still operate under the assumption of uniform computing resources. This leads to load imbalance that degrades the overall performance. Even with a balanced data distribution, the irregularity of graph applications can result in different amounts of dynamic operations on each machine in the cluster. Such imbalanced work distribution slows down the execution speed. Besides these, redundancy also impacts the performance of distributed graph analysis. To utilize the available parallelism of computing clusters, distributed graph systems deploy the repeated-relaxing computation model (e.g., Bellman-Ford algorithm variants) rather than in a sequential but work-optimal order. Studies performed in this dissertation show that redundant computations pervasively exist and significantly impact the performance efficiency of distributed graph processing. This dissertation explores novel techniques to reduce the workload imbalance and redundant computations of analyzing large-scale graphs in a distributed setup. It evaluates proposed techniques on both pre-processing and execution modules to enable fair data distribution, lightweight workload balancing, and redundancy optimization for future distributed graph processing systems. The first contribution of this dissertation is the Heterogeneity-aware Partitioning (HAP) that aims to balance load distribution of distributed graph processing in heterogeneous clusters. HAP proposes a number of methodologies to estimate various machines’ computational power on graph analytics. It also extends several state-of-the-art partitioning algorithms for heterogeneity-aware data distribution. Utilizing the estimation of machines’ graph processing capability to guide extended partitioning algorithms can reduce load imbalance when processing a large-scale graph in heterogeneous clusters. This results in significant performance improvement. Another contribution of the dissertation is the Hula, which optimizes the workload balance of distributed graph analytics on the fly. Hula offers a hybrid graph partitioning algorithm to split a large-scale graph in a locality-friendly manner and generate metadata for lightweight dynamic workload balancing. To track machines’ work intensity, Hula inserts hardware timers to count the time spent on the important operations (e.g., computational operations and atomic operations). This information can guide Hula’s workload scheduler to arrange work migration. With the support of metadata generated by the hybrid partitioner, Hula’s migration scheme only requires a minimal amount of data to transfer work between machines in the cluster. Hula’s workload balancing design achieves a lightweight imbalance reduction on the fly. Finally, this dissertation focuses on improving the computational efficiency of distributed graph processing. To do so, it reveals the root cause and the amount of redundant computations in distributed graph processing. SLFE is proposed as a system solution to reduce these redundant operations. SLFE develops a lightweight pre-processing technique to obtain the maximum propagation order of each vertex in a given graph. This information is defined as Redundancy Reduction Guidance (RRG) and is utilized by SLFE’s Redundancy Reduction (RR)-aware computing model to prune redundant operations on the fly. Moreover, SLFE provides RRaware APIs to maintain high promgrammablity. These techniques allow the redundancy optimizations of distributed graph processing to become transparent to users

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Distributed Graph Analytics

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