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Distributed graph analyticsprogrammi...

Cheramangalath, Unnikrishnan.

Distributed graph analyticsprogramming, languages, and their compilation /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Distributed graph analyticsby Unnikrishnan Cheramangalath, Rupesh Nasre, Y. N. Srikant.
其他題名:	programming, languages, and their compilation /
作者:	Cheramangalath, Unnikrishnan.
其他作者:	Nasre, Rupesh.
出版者:	Cham :Springer International Publishing :2020.
面頁冊數:	xi, 207 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Graph algorithms.
電子資源:	https://doi.org/10.1007/978-3-030-41886-1
ISBN:	9783030418861$q(electronic bk.)

Distributed graph analyticsprogramming, languages, and their compilation /
Cheramangalath, Unnikrishnan.

Distributed graph analyticsprogramming, languages, and their compilation /[electronic resource] :by Unnikrishnan Cheramangalath, Rupesh Nasre, Y. N. Srikant. - Cham :Springer International Publishing :2020. - xi, 207 p. :ill., digital ;24 cm.

Introduction to Graph Analytics -- Graph Algorithms and Applications -- Efficient Parallel Implementation of Graph Algorithms -- Graph Analytics Frameworks -- GPU Architecture and Programming Challenges -- Dynamic Graph Algorithms -- Falcon: A Domain Specific Language for Graph Analytics -- Experiments, Evaluation and Future Directions.

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.

ISBN: 9783030418861$q(electronic bk.)

Standard No.: 10.1007/978-3-030-41886-1doiSubjects--Topical Terms:

455716
Graph algorithms.

LC Class. No.: QA166.245 / .C44 2020

Dewey Class. No.: 518.1

Distributed graph analyticsprogramming, languages, and their compilation /
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520 $a 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.
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