A Distributed Divide and Conquer Skeleton
- Juan R. GonzálezAffiliated withDpto. Estadítica, I.O. y Computación, Universidad de La Laguna
- , Coromoto LeónAffiliated withDpto. Estadítica, I.O. y Computación, Universidad de La Laguna
- , Casiano RodríguezAffiliated withDpto. Estadítica, I.O. y Computación, Universidad de La Laguna
The MaLLBa library provides skeletons to solve combinatorial optimization problems. Its main objective is to simplify the implementation of algorithms based on some commonly used techniques such as Branch and Bound, Dynamic Programming and Divide and Conquer.
This work is focused on the MaLLBa::DnC skeleton, which solves problems that fit in the Divide and Conquer paradigm. The user has to provide functions particularized to the problem he wants to solve. Given that functions the skeleton encapsulates all remaining work and allows the problem to be solved either in a sequential or parallel way.
In this work we will present a new mpi asynchronous peer-processor implementation of the MaLLBa::DnC skeleton where all processors are peers and behave the same way (except during the initialization phase) and where decisions are taken based only on local information. Results on a Linux cluster of PC for matrix and huge integer multiplication are presented.
- A Distributed Divide and Conquer Skeleton
- Book Title
- Applied Parallel Computing. State of the Art in Scientific Computing
- Book Subtitle
- 7th International Workshop, PARA 2004, Lyngby, Denmark, June 20-23, 2004. Revised Selected Papers
- pp 481-489
- Print ISBN
- Online ISBN
- Series Title
- Lecture Notes in Computer Science
- Series Volume
- Series ISSN
- Springer Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
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- Industry Sectors
- eBook Packages
- Editor Affiliations
- 16. Computer Science Department, University of Tennessee
- 17. Department of Informatics and Mathematical Modelling, Technical University of Denmark
- 18. Informatics & Mathematical Modeling, Technical University of Denmark
- Author Affiliations
- 19. Dpto. Estadítica, I.O. y Computación, Universidad de La Laguna, E-38271, La Laguna, Tenerife, Spain
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