-
Erik G. Boman,
Doruk Bozdag,
Umit Catalyurek,
Assefaw H. Gebremedhin,
and Fredrik Manne.
A Scalable Parallel Graph Coloring Algorithm for Distributed Memory Computers.
In Lecture Notes in Computer Science,
volume 3648,
pages 241--251,
August 2005.
@InProceedings{Boman05:Coloring,
author = {Erik G. Boman and Doruk Bozda{\u{g}} and Umit Catalyurek and Assefaw H. Gebremedhin and Fredrik Manne},
title = {A Scalable Parallel Graph Coloring Algorithm for Distributed Memory Computers},
booktitle = {Lecture Notes in Computer Science},
pages = {241--251},
year = 2005,
volume = 3648,
month = {August}
}
-
Guojing Cong and David A. Bader.
An Experimental Study of Parallel Biconnected Components Algorithms on Symmetric Multiprocessors (SMPs).
In 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05),
2005.
[PDF
]
Keywords:
Biconnected components.
@InProceedings{CongBaderBiconnected05,
author = {Guojing Cong and David A. Bader},
title = {An Experimental Study of Parallel Biconnected Components Algorithms on Symmetric Multiprocessors ({SMPs})},
booktitle = {19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05)},
year = 2005,
pdf = {../papers/CongBaderBiconnected05.pdf},
keywords = {Biconnected components}
}
-
Douglas Gregor and Andrew Lumsdaine.
The Parallel BGL: A Generic Library for Distributed Graph Computations.
In In Proceedings of the Fourth Workshop on Parallel Object-Oriented Scientific Computing,
July 2005.
| Annotation: |
This paper presents the Parallel BGL, a generic C++ library for distributed graph computation. Like the sequential Boost Graph Library (BGL) upon which it is based, the Parallel BGL applies the paradigm of generic programming to the domain of graph computations. Emphasizing efficient generic algorithms and the use of concepts to specify the requirements on type parameters, the Parallel BGL also provides flexible supporting data structures such as distributed adjacency lists and external property maps. The generic programming approach simultaneously stresses flexibility and efficiency, resulting in a parallel graph library that can adapt to various data structures and communication models while retaining the efficiency of equivalent hand-coded programs. Performance data for selected algorithms are provided demonstrating the efficiency and scalability of the Parallel BGL. |
@InProceedings{Gregor:POOSC:2005,
author = {Douglas Gregor and Andrew Lumsdaine},
title = {The {Parallel} {BGL}: {A} Generic Library for Distributed Graph Computations},
booktitle = {In Proceedings of the Fourth Workshop on Parallel Object-Oriented Scientific Computing},
year = 2005,
month = {July},
thanks = {NSF grant EIA-0131354 and by a grant from the Lilly Endowment},
annote = {This paper presents the Parallel BGL, a generic C++ library for distributed graph computation. Like the sequential Boost Graph Library (BGL) upon which it is based, the Parallel BGL applies the paradigm of generic programming to the domain of graph computations. Emphasizing efficient generic algorithms and the use of concepts to specify the requirements on type parameters, the Parallel BGL also provides flexible supporting data structures such as distributed adjacency lists and external property maps. The generic programming approach simultaneously stresses flexibility and efficiency, resulting in a parallel graph library that can adapt to various data structures and communication models while retaining the efficiency of equivalent hand-coded programs. Performance data for selected algorithms are provided demonstrating the efficiency and scalability of the Parallel BGL.}
}
-
Douglas Gregor and Andrew Lumsdaine.
The Execution Instance Overloading Pattern.
In Workshop on Patterns in High-Performance Computing,
2005.
@InProceedings{Gregor05:eio_pattern,
author = {Douglas Gregor and Andrew Lumsdaine},
title = {The Execution Instance Overloading Pattern},
booktitle = {Workshop on Patterns in High-Performance Computing},
year = 2005
}
-
Douglas Gregor and Andrew Lumsdaine.
Lifting Sequential Graph Algorithms for Distributed-Memory Parallel Computation.
In Proceedings of the 2005 ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications,
pages 423--437,
October 2005.
@InProceedings{Gregor:OOPSLA:2005,
author = {Douglas Gregor and Andrew Lumsdaine},
title = {Lifting Sequential Graph Algorithms for Distributed-Memory Parallel Computation},
booktitle = {Proceedings of the 2005 {ACM} {SIGPLAN} conference on {O}bject-oriented programming, systems, languages, and applications},
pages = {423--437},
month = {October},
year = 2005,
thanks = {NSF grant EIA-0131354 and by a grant from the Lilly Endowment}
}
-
Qiaofeng Yang and Stefano Lonardi.
A Parallel Algorithm for Clustering Protein-Protein Interaction Networks.
In CSBW '05: Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference - Workshops,
Washington, DC, USA,
pages 174--177,
2005.
IEEE Computer Society.
[WWW
]
@inproceedings{Yang05,
author = {Yang, Qiaofeng and Lonardi, Stefano},
title = {A Parallel Algorithm for Clustering Protein-Protein Interaction Networks},
booktitle = {CSBW '05: Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference - Workshops},
year = {2005},
isbn = {0-7695-2442-7},
pages = {174--177},
doi = {http://dx.doi.org/10.1109/CSBW.2005.13},
publisher = {IEEE Computer Society},
address = {Washington, DC, USA},
}