Authors: Harshvardhan, Adam Fidel, Nancy M. Amato, Lawrence Rauchwerger

Venue: Languages and Compilers for Parallel Computing (LCPC)
DOI: 10.1007/978-3-642-37658-0_4
Link to Publication

Abstract:
This paper describes the stapl Parallel Graph Library, a high-level framework that abstracts the user from data-distribution and parallelism details and allows them to concentrate on parallel graph algorithm development. It includes a customizable distributed graph container and a collection of commonly used parallel graph algorithms. The library introduces pGraph pViews that separate algorithm design from the container implementation. It supports three graph processing algorithmic paradigms, level-synchronous, asynchronous and coarse-grained, and provides common graph algorithms based on them. Experimental results demonstrate improved scalability in performance and data size over existing graph libraries on more than 16,000 cores and on internet-scale graphs containing over 16 billion vertices and 250 billion edges. 

@InProceedings{10.1007/978-3-642-37658-0_4,

author=\"Harshvardhan

and Fidel, Adam

and Amato, Nancy M.

and Rauchwerger, Lawrence\",

editor=\"Kasahara, Hironori

and Kimura, Keiji\",

title=\"The STAPL Parallel Graph Library\",

booktitle=\"Languages and Compilers for Parallel Computing\",

year=\"2013\",

publisher=\"Springer Berlin Heidelberg\",

address=\"Berlin, Heidelberg\",

pages=\"46--60\",

abstract=\"This paper describes the stapl Parallel Graph Library, a high-level framework that abstracts the user from data-distribution and parallelism details and allows them to concentrate on parallel graph algorithm development. It includes a customizable distributed graph container and a collection of commonly used parallel graph algorithms. The library introduces pGraphpViews that separate algorithm design from the container implementation. It supports three graph processing algorithmic paradigms, level-synchronous, asynchronous and coarse-grained, and provides common graph algorithms based on them. Experimental results demonstrate improved scalability in performance and data size over existing graph libraries on more than 16,000 cores and on internet-scale graphs containing over 16 billion vertices and 250 billion edges.\",

isbn=\"978-3-642-37658-0\"

}