作者: 輕推 時間: 2025-3-21 22:44 作者: Radiation 時間: 2025-3-22 02:41
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作者: faction 時間: 2025-3-22 08:23 作者: 使習慣于 時間: 2025-3-22 09:09
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.作者: 色情 時間: 2025-3-22 16:03
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.作者: 色情 時間: 2025-3-22 20:34
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.作者: 沙發(fā) 時間: 2025-3-23 00:48 作者: CHAR 時間: 2025-3-23 04:08
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.作者: 托運 時間: 2025-3-23 06:00 作者: Petechiae 時間: 2025-3-23 09:58
: A Domain Specific Language for Graph Analytics,The 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.作者: 象形文字 時間: 2025-3-23 16:37 作者: Flat-Feet 時間: 2025-3-23 21:07 作者: 得意牛 時間: 2025-3-24 00:58
https://doi.org/10.1057/9780230270688 like traversals, shortest paths, etc., more specialized algorithms such as betweenness centrality, page rank, etc. follow. The chapter ends with a focused discussion of applications of graph analytics in different domains such as graph mining and graph databases.作者: essential-fats 時間: 2025-3-24 04:46
https://doi.org/10.1057/9780230270688dels of execution that are used in graph analytics, such as BSP, Map-Reduce, asynchronous execution, GAS, Inspector-Executor, and Advance-Filter-Compute. It also provides a glimpse of different existing frameworks on multi-core CPUs, GPUs, and distributed systems.作者: obligation 時間: 2025-3-24 07:06 作者: 解凍 時間: 2025-3-24 14:35 作者: compassion 時間: 2025-3-24 17:31
Graph Algorithms and Applications, like traversals, shortest paths, etc., more specialized algorithms such as betweenness centrality, page rank, etc. follow. The chapter ends with a focused discussion of applications of graph analytics in different domains such as graph mining and graph databases.作者: insipid 時間: 2025-3-24 22:08 作者: sparse 時間: 2025-3-25 02:32
Dynamic Graph Algorithms,d deletion of edges and vertices, and the query for property values relevant to the algorithm. The efficiency of a dynamic algorithm depends on the data structure used to implement it. This chapter provides a glimpse into this exciting area in graph analytics.作者: 詼諧 時間: 2025-3-25 07:01
https://doi.org/10.1057/9780230270688ns, programming frameworks for parallelization, and various challenges posed by graph analytics algorithms. Graph partitioning and real-world applications of graphs are also covered. Frameworks and DSLs that can ease programming graph analytics are briefly discussed.作者: 匍匐前進 時間: 2025-3-25 10:37
https://doi.org/10.1057/9780230270688 like traversals, shortest paths, etc., more specialized algorithms such as betweenness centrality, page rank, etc. follow. The chapter ends with a focused discussion of applications of graph analytics in different domains such as graph mining and graph databases.作者: JECT 時間: 2025-3-25 12:52 作者: Confidential 時間: 2025-3-25 16:12 作者: 相互影響 時間: 2025-3-25 22:36
Unnikrishnan Cheramangalath,Rupesh Nasre,Y. N. SriPresents 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作者: 致詞 時間: 2025-3-26 01:16 作者: colony 時間: 2025-3-26 07:56 作者: Highbrow 時間: 2025-3-26 10:09 作者: Ventricle 時間: 2025-3-26 13:49
Introduction to Graph Analytics,ns, programming frameworks for parallelization, and various challenges posed by graph analytics algorithms. Graph partitioning and real-world applications of graphs are also covered. Frameworks and DSLs that can ease programming graph analytics are briefly discussed.作者: SLUMP 時間: 2025-3-26 19:38
Graph Algorithms and Applications, like traversals, shortest paths, etc., more specialized algorithms such as betweenness centrality, page rank, etc. follow. The chapter ends with a focused discussion of applications of graph analytics in different domains such as graph mining and graph databases.作者: 原來 時間: 2025-3-26 20:56 作者: 嚴重傷害 時間: 2025-3-27 04:21 作者: JIBE 時間: 2025-3-27 06:17
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9樓作者: 首創(chuàng)精神 時間: 2025-3-28 00:59
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10樓作者: nonradioactive 時間: 2025-3-28 07:02
10樓作者: 散開 時間: 2025-3-28 14:09
10樓
