TY - GEN
T1 - The stapl Parallel Graph Library
AU - Harshvardhan,
AU - Fidel, Adam
AU - Amato, Nancy M.
AU - Rauchwerger, Lawrence
N1 - Funding Information:
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, by DOE NNSA under the Predictive Science Academic Alliances Program grant DE-FC52-08NA28616, by THECB NHARP award 000512-0097-2009, by Chevron, IBM, Intel, Oracle/Sun and by Award KUS-C1-016-04 made by King Abdullah University of Science and Technology (KAUST). This research used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.
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.
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.
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U2 - 10.1007/978-3-642-37658-0_4
DO - 10.1007/978-3-642-37658-0_4
M3 - Conference contribution
AN - SCOPUS:84893113749
SN - 9783642376573
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 46
EP - 60
BT - Languages and Compilers for Parallel Computing - 25th International Workshop, LCPC 2012, Revised Selected Papers
T2 - 25th International Workshop on Languages and Compilers for Parallel Computing, LCPC 2012
Y2 - 11 September 2012 through 13 September 2012
ER -