Paper Info
Title
PGAS for graph analytics: can one sided communications break the scalability barrier?
Abstract
As the world is becoming increasingly interconnected and systems increasingly complex. Therefore, technologies that can analyze connected systems and their dynamic characteristics become indispensable. Consequently, the last decade has seen increasing interest in graph analytics, which allows obtaining insights from such connected data. Parallel graph analytics can reveal the workings of intricate systems and networks at massive scales, which are found in diverse areas such as social networks, economic transactions, and protein interactions. While sequential graph algorithms have been studied for decades, the recent availability of massive datasets has given rise to the need for parallel graph processing, which poses unique challenges.
Year
DOI
Venue
2019
10.1145/3310273.3324293
Proceedings of the 16th ACM International Conference on Computing Frontiers
Field
DocType
ISBN
Graph,Graph algorithms,Social network,Computer science,Parallel computing,Graph analytics,Partitioned global address space,Scalability,Distributed computing
Conference
978-1-4503-6685-4
Citations 
PageRank 
References 
0
0.34
0
Authors
1
Name
Order
Citations
PageRank
Johannes Langguth18512.71