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Author : Naga Shailaja Dasari
Publisher :
Page : 210 pages
File Size : 31,75 MB
Release : 2015
Category : Charts, diagrams, etc
ISBN :
Author : David A. Bader
Publisher : CRC Press
Page : 632 pages
File Size : 10,94 MB
Release : 2022-07-20
Category : Business & Economics
ISBN : 1000538613
"Graphs. Such a simple idea. Map a problem onto a graph then solve it by searching over the graph or by exploring the structure of the graph. What could be easier? Turns out, however, that working with graphs is a vast and complex field. Keeping up is challenging. To help keep up, you just need an editor who knows most people working with graphs, and have that editor gather nearly 70 researchers to summarize their work with graphs. The result is the book Massive Graph Analytics." — Timothy G. Mattson, Senior Principal Engineer, Intel Corp Expertise in massive-scale graph analytics is key for solving real-world grand challenges from healthcare to sustainability to detecting insider threats, cyber defense, and more. This book provides a comprehensive introduction to massive graph analytics, featuring contributions from thought leaders across academia, industry, and government. Massive Graph Analytics will be beneficial to students, researchers, and practitioners in academia, national laboratories, and industry who wish to learn about the state-of-the-art algorithms, models, frameworks, and software in massive-scale graph analytics.
Author : Yingxia Shao
Publisher : Springer Nature
Page : 154 pages
File Size : 15,53 MB
Release : 2020-07-01
Category : Computers
ISBN : 9811539286
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.
Author : Da Yan
Publisher : Springer
Page : 93 pages
File Size : 31,58 MB
Release : 2017-05-31
Category : Computers
ISBN : 3319582178
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.
Author : Nicole Celeste Rodia
Publisher :
Page : pages
File Size : 10,88 MB
Release : 2019
Category :
ISBN :
The rise of big data analytics has contributed to the growing popularity and scale of graph datasets, positioning graph analysis as an important research area. Graph analysis is an essential tool in many domains, including the physical and social sciences, healthcare, business intelligence, and cybersecurity. The increasing scale of graph analysis problems, with graphs containing millions or billions of vertices and edges, has made parallel and distributed graph algorithms essential for effective analysis of these large datasets. At the same time, modern multicore systems have been scaling to higher core counts, with dozens of complex cores in a single system. At first glance, it would seem that graph algorithms can leverage data-level parallelism across graph vertices and edges to utilize this large number of cores to quickly process large datasets. In fact, on multicore systems, graph algorithms are typically inefficient and perform poorly. The real-world informatics graphs used for today's big data analytics are derived from online social networks, web page links, genomics data, and the like. These networks possess fundamental properties that differ from traditional graphs like trees or meshes, resulting in different execution characteristics. We study the factors behind this lack of performance and demonstrate software and hardware techniques that improve performance. First, we analyze the perfor- mance characteristics of a core set of graph analysis algorithms across several infor- matics, physical, and synthetic graph datasets using a multicore microarchitectural simulator. Our characterization indicates that poor performance is due to several fac- tors, including irregular data access patterns, load imbalance, high communication- to-computation ratio, and ineffective caching techniques. To investigate the potential for caching to improve graph algorithm performance, we study the algorithms' data locality. Cache miss rates are an unreliable metric for data locality because they are heavily influenced by dataset size, cache size, and replacement policy. Thus, we use cache-independent locality analysis techniques, including reuse distance and a probability-based locality score, to analyze data locality in graph algorithms. Based on our analysis of data locality, we find that LRU-based cache replacement policies do not provide good performance for the data access patterns characteristic of graph algorithms. Further, we show that data access patterns correlate with algorithm characteristics, graph dataset structure, and vertex degree. These insights indicate that utilization of algorithm- and dataset-specific locality information paired with an improved cache replacement policy could significantly improve graph algorithm performance. Second, we employ our knowledge of real-world graph properties to redesign the algorithm for detecting strongly connected components (SCCs) in a directed graph, a fundamental graph analysis algorithm used in many scientific and engineering do- mains. Traditional approaches in parallel SCC detection show limited performance and poor scaling behavior when applied to large real-world graph instances. We investigate the shortcomings of the conventional approach and propose a series of ex- tensions that account for the fundamental properties of real-world graphs, particularly the small-world property. Our scalable implementation offers excellent performance on diverse small-world graphs resulting in a factor of 5 to 29 times parallel speedup over an optimal sequential algorithm on 16 cores and 32 hardware threads. Third, we propose a new cache replacement policy based on our observations of data locality in graph algorithms. The Graph Priority Insertion Policy (GPIP) uses per-data-structure software priority hints to improve last-level cache hit rates by maintaining data with higher locality in the cache. This policy provides an average reduction in misses per thousand instruction (MPKI) of 3% over least-recently used (LRU) replacement. Overall, our contributions serve to expand understanding of the characteristics of graph algorithms and improve graph algorithm performance through both software and hardware means.
Author : Jeffrey S. Vetter
Publisher : CRC Press
Page : 434 pages
File Size : 13,48 MB
Release : 2019-04-30
Category : Computers
ISBN : 135103684X
Contemporary High Performance Computing: From Petascale toward Exascale, Volume 3 focuses on the ecosystems surrounding the world’s leading centers for high performance computing (HPC). It covers many of the important factors involved in each ecosystem: computer architectures, software, applications, facilities, and sponsors. This third volume will be a continuation of the two previous volumes, and will include other HPC ecosystems using the same chapter outline: description of a flagship system, major application workloads, facilities, and sponsors. Features: Describes many prominent, international systems in HPC from 2015 through 2017 including each system’s hardware and software architecture Covers facilities for each system including power and cooling Presents application workloads for each site Discusses historic and projected trends in technology and applications Includes contributions from leading experts Designed for researchers and students in high performance computing, computational science, and related areas, this book provides a valuable guide to the state-of-the art research, trends, and resources in the world of HPC.
Author : Dhabaleswar K. Panda
Publisher : MIT Press
Page : 275 pages
File Size : 20,46 MB
Release : 2022-08-02
Category : Computers
ISBN : 0262046857
An in-depth overview of an emerging field that brings together high-performance computing, big data processing, and deep lLearning. Over the last decade, the exponential explosion of data known as big data has changed the way we understand and harness the power of data. The emerging field of high-performance big data computing, which brings together high-performance computing (HPC), big data processing, and deep learning, aims to meet the challenges posed by large-scale data processing. This book offers an in-depth overview of high-performance big data computing and the associated technical issues, approaches, and solutions. The book covers basic concepts and necessary background knowledge, including data processing frameworks, storage systems, and hardware capabilities; offers a detailed discussion of technical issues in accelerating big data computing in terms of computation, communication, memory and storage, codesign, workload characterization and benchmarking, and system deployment and management; and surveys benchmarks and workloads for evaluating big data middleware systems. It presents a detailed discussion of big data computing systems and applications with high-performance networking, computing, and storage technologies, including state-of-the-art designs for data processing and storage systems. Finally, the book considers some advanced research topics in high-performance big data computing, including designing high-performance deep learning over big data (DLoBD) stacks and HPC cloud technologies.
Author : Kai Wang
Publisher :
Page : 146 pages
File Size : 15,31 MB
Release : 2019
Category :
ISBN :
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.
Author : Pethuru Raj
Publisher : Springer
Page : 443 pages
File Size : 29,33 MB
Release : 2015-10-16
Category : Computers
ISBN : 331920744X
This book presents a detailed review of high-performance computing infrastructures for next-generation big data and fast data analytics. Features: includes case studies and learning activities throughout the book and self-study exercises in every chapter; presents detailed case studies on social media analytics for intelligent businesses and on big data analytics (BDA) in the healthcare sector; describes the network infrastructure requirements for effective transfer of big data, and the storage infrastructure requirements of applications which generate big data; examines real-time analytics solutions; introduces in-database processing and in-memory analytics techniques for data mining; discusses the use of mainframes for handling real-time big data and the latest types of data management systems for BDA; provides information on the use of cluster, grid and cloud computing systems for BDA; reviews the peer-to-peer techniques and tools and the common information visualization techniques, used in BDA.
Author : Da Yan
Publisher :
Page : 218 pages
File Size : 22,36 MB
Release : 2017-01-12
Category : Computers
ISBN : 9781680832426
A comprehensive survey that clearly summarizes the key features and techniques developed in existing big graph systems. It aims to help readers get a systematic picture of the landscape of recent big graph systems, focusing not just on the systems themselves, but also on the key innovations and design philosophies underlying them.