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Bioinformatics
Article . 2011 . Peer-reviewed
Data sources: Crossref
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Bioinformatics
Article . 2011
DBLP
Article . 2011
Data sources: DBLP
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Faster exact maximum parsimony search with XMP

Authors: W. Timothy J. White; Barbara R. Holland;

Faster exact maximum parsimony search with XMP

Abstract

AbstractMotivation: Despite trends towards maximum likelihood and Bayesian criteria, maximum parsimony (MP) remains an important criterion for evaluating phylogenetic trees. Because exact MP search is NP-complete, the computational effort needed to find provably optimal trees skyrockets with increasing numbers of taxa, limiting analyses to around 25–30 taxa. This is, in part, because currently available programs fail to take advantage of parallelism.Results: We present XMP, a new program for finding exact MP trees that comes in both serial and parallel versions. The serial version is faster in nearly all tests than existing software. The parallel version uses a work-stealing algorithm to scale to hundreds of CPUs on a distributed-memory multiprocessor with high efficiency. An optimized SSE2 inner loop provides additional speedup for Pentium 4 and later CPUs.Availability: C source code and several binary versions are freely available from http://www.massey.ac.nz/~wtwhite/xmp. The parallel version requires an MPI implementation, such as the freely available MPICH2.Contact: w.t.white@massey.ac.nz; barbara.holland@utas.edu.auSupplementary information: Supplementary data are available at Bioinformatics online.

Related Organizations
Keywords

Likelihood Functions, Animals, Humans, Bayes Theorem, Algorithms, Phylogeny, Software

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
10
Average
Average
Top 10%
gold