scholarly journals GraphH: High Performance Big Graph Analytics in Small Clusters

2017 ◽  
Author(s):  
Peng Sun ◽  
Yonggang Wen ◽  
Ta Nguyen Binh Duong ◽  
Xiaokui Xiao
Keyword(s):  
High Performance ◽  
Graph Analytics ◽  
Big Graph ◽  
Small Clusters

scholarly journals Big Graph Analytics Systems

2016 ◽  
Author(s):  
Da Yan ◽  
Yingyi Bu ◽  
Yuanyuan Tian ◽  
Amol Deshpande ◽  
James Cheng
Keyword(s):  
Graph Analytics ◽  
Big Graph

Big Graph Analytics

2018 ◽  
pp. 97-104
Author(s):  
Ahsanur Rahman ◽  
Tamanna Motahar
Keyword(s):  
Graph Analytics ◽  
Big Graph

High-Performance and Energy-Efficient Network-on-Chip Architectures for Graph Analytics

2016 ◽  
Vol 15 (4) ◽  
pp. 1-26 ◽  
Author(s):  
Karthi Duraisamy ◽  
Hao Lu ◽  
Partha Pratim Pande ◽  
Ananth Kalyanaraman
Keyword(s):  
Energy Efficient ◽  
High Performance ◽  
Network On Chip ◽  
Graph Analytics ◽  
On Chip

Systems for Big Graph Analytics

2017 ◽  
Author(s):  
Da Yan ◽  
Yuanyuan Tian ◽  
James Cheng
Keyword(s):  
Graph Analytics ◽  
Big Graph

Big Graph Analytics Platforms

2017 ◽  
Vol 7 (1-2) ◽  
pp. 1-195 ◽  
Author(s):  
Da Yan ◽  
Yingyi Bu ◽  
Yuanyuan Tian ◽  
Amol Deshpande
Keyword(s):  
Graph Analytics ◽  
Big Graph

scholarly journals Grafs: declarative graph analytics

2021 ◽  
Vol 5 (ICFP) ◽  
pp. 1-32
Author(s):  
Farzin Houshmand ◽  
Mohsen Lesani ◽  
Keval Vora
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Kernel Functions ◽  
Runtime Systems ◽  
Graph Processing ◽  
Large Graphs ◽  
Graph Analytics ◽  
Efficient Code ◽  
High Level ◽  
Abstract Interface

Graph analytics elicits insights from large graphs to inform critical decisions for business, safety and security. Several large-scale graph processing frameworks feature efficient runtime systems; however, they often provide programming models that are low-level and subtly different from each other. Therefore, end users can find implementation and specially optimization of graph analytics error-prone and time-consuming. This paper regards the abstract interface of the graph processing frameworks as the instruction set for graph analytics, and presents Grafs, a high-level declarative specification language for graph analytics and a synthesizer that automatically generates efficient code for five high-performance graph processing frameworks. It features novel semantics-preserving fusion transformations that optimize the specifications and reduce them to three primitives: reduction over paths, mapping over vertices and reduction over vertices. Reductions over paths are commonly calculated based on push or pull models that iteratively apply kernel functions at the vertices. This paper presents conditions, parametric in terms of the kernel functions, for the correctness and termination of the iterative models, and uses these conditions as specifications to automatically synthesize the kernel functions. Experimental results show that the generated code matches or outperforms handwritten code, and that fusion accelerates execution.

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