DocumentCode :
3648290
Title :
Scalable complex graph analysis with the knowledge discovery toolbox
Author :
Adam Lugowski;Aydın Buluç;John R. Gilbert;Steve Reinhardt
Author_Institution :
University of California, Santa Barbara, USA
fYear :
2012
fDate :
3/1/2012 12:00:00 AM
Firstpage :
5345
Lastpage :
5348
Abstract :
The Knowledge Discovery Toolbox (KDT) enables domain experts to perform complex analyses of huge datasets on supercomputers using a high-level language without grappling with the difficulties of writing parallel code, calling parallel libraries, or becoming a graph expert. KDT delivers competitive performance from a general-purpose, reusable library for graphs on the order of 10 billion edges and greater. We describe our approach for supporting arbirary vertex and edge attributes, in-place graph filtering, and graph traversal using pre-defined access patterns.
Keywords :
"Vectors","Semantics","Algorithm design and analysis","Sparse matrices","Libraries","Clustering algorithms"
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on
ISSN :
1520-6149
Print_ISBN :
978-1-4673-0045-2
Type :
conf
DOI :
10.1109/ICASSP.2012.6289128
Filename :
6289128
Link To Document :
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