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.
Bibliographical noteKAUST 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.