TY - CHAP
T1 - The STAPL Parallel Graph Library
AU - Harshvardhan,
AU - Fidel, Adam
AU - Amato, Nancy M.
AU - Rauchwerger, Lawrence
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: This research supported in part by NSF awards CRI-0551685, CCF-0833199, CCF-0830753, IIS-096053, IIS-0917266, NSF/DNDO award 2008-DN-077-ARI018-02, byDOE NNSA under the Predictive Science Academic Alliances Program grant DE-FC52-08NA28616, by THECBNHARP award000512-0097-2009, by Chevron, IBM,Intel, Oracle/Sun and by Award KUS-C1-016-04 made by King Abdullah Universityof Science and Technology (KAUST). This research used resources of the NationalEnergy Research Scientific Computing Center, which is supported by the Office ofScience of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.
PY - 2013
Y1 - 2013
N2 - 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. © Springer-Verlag Berlin Heidelberg 2013.
AB - 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. © Springer-Verlag Berlin Heidelberg 2013.
UR - http://hdl.handle.net/10754/599963
UR - http://link.springer.com/10.1007/978-3-642-37658-0_4
UR - http://www.scopus.com/inward/record.url?scp=84893113749&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-37658-0_4
DO - 10.1007/978-3-642-37658-0_4
M3 - Chapter
SN - 9783642376573
SP - 46
EP - 60
BT - Languages and Compilers for Parallel Computing
PB - Springer Nature
ER -