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Titlebook: Distributed Graph Analytics; Programming, Languag Unnikrishnan Cheramangalath,Rupesh Nasre,Y. N. Sri Book 2020 Springer Nature Switzerland

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發(fā)表于 2025-3-21 16:44:17 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Distributed Graph Analytics
副標題Programming, Languag
編輯Unnikrishnan Cheramangalath,Rupesh Nasre,Y. N. Sri
視頻videohttp://file.papertrans.cn/282/281899/281899.mp4
概述Presents the essentials of efficiently combining graph analysis algorithms and high performance computing.Describes several parallel algorithms used in distributed graph analytics in a high level nota
圖書封面Titlebook: Distributed Graph Analytics; Programming, Languag Unnikrishnan Cheramangalath,Rupesh Nasre,Y. N. Sri Book 2020 Springer Nature Switzerland
描述.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 differe
出版日期Book 2020
關鍵詞Graph Algorithms; Software Engineering; Model-Driven Software Engineering; Domain-Specific Languages; Co
版次1
doihttps://doi.org/10.1007/978-3-030-41886-1
isbn_softcover978-3-030-41888-5
isbn_ebook978-3-030-41886-1
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 22:44:14 | 只看該作者
板凳
發(fā)表于 2025-3-22 02:41:39 | 只看該作者
hms used in distributed graph analytics in a high level nota.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: algorit
地板
發(fā)表于 2025-3-22 08:23:54 | 只看該作者
5#
發(fā)表于 2025-3-22 09:09:41 | 只看該作者
https://doi.org/10.1057/9780230270688This chapter provides some insights into the issues in programming parallel algorithms. Parallelism, atomicity, push and pull types of computation, algorithms driven by topology or data, vertex based, edge based and worklist based computation, are some of the important considerations in designing parallel implementations.
6#
發(fā)表于 2025-3-22 16:03:11 | 只看該作者
https://doi.org/10.1057/9780230270688This chapter provides an overview of GPU architectures and CUDA programming. The performance of the same graph algorithms on multi-core CPU and GPU are usually very different. Intricacies of thread scheduling, barrier synchronization, warp based execution, memory hierarchy, and their effects on graph analytics are illustrated with simple examples.
7#
發(fā)表于 2025-3-22 20:34:38 | 只看該作者
https://doi.org/10.1057/9780230270688The domain-specific language . is presented in this chapter. The data types and statements of . that support easy programming of graph analytics applications are described. To drive home the point that . programs can be very efficient, code generation mechanisms used in the . compiler are delineated with examples.
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發(fā)表于 2025-3-23 00:48:15 | 只看該作者
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發(fā)表于 2025-3-23 04:08:48 | 只看該作者
Efficient Parallel Implementation of Graph Algorithms,This chapter provides some insights into the issues in programming parallel algorithms. Parallelism, atomicity, push and pull types of computation, algorithms driven by topology or data, vertex based, edge based and worklist based computation, are some of the important considerations in designing parallel implementations.
10#
發(fā)表于 2025-3-23 06:00:15 | 只看該作者
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