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Related Experiment Videos

Efficiency of parallel direct optimization.

D A Janies1, W C Wheeler

  • 1Division of Invertebrate Zoology, American Museum of Natural History, New York, New York 10024, USA. djanies@amnh.org

Cladistics : the International Journal of the Willi Hennig Society
|September 21, 2002
PubMed
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Parallel computing in phylogenetics shows varied efficiency. Multibuilding is efficient on large clusters, while parallel building is not, regardless of data size.

Area of Science:

  • Computational phylogenetics
  • Parallel computing algorithms

Background:

  • Sequential computation in phylogenetics has advanced significantly.
  • Parallel computing offers potential for large-scale phylogenetic analyses due to inherent task parallelism.

Purpose of the Study:

  • To investigate the scaling factors and efficiency of parallel algorithms in phylogenetic tree reconstruction.
  • To evaluate the performance of direct optimization software (POY) on different cluster sizes.

Main Methods:

  • Testing random addition and tree refinement strategies using POY software.
  • Utilizing small (10 slave processors) and large (256 slave processors) Linux PC clusters.
  • Analyzing DNA and morphology datasets with 40 to 500 taxa.

Main Results:

Keywords:
NASA Discipline Evolutionary BiologyNon-NASA Center

Related Experiment Videos

  • Multibuilding algorithms demonstrate excellent parallel efficiency on large clusters; parallel building is inefficient.
  • Parallel branch swapping shows speed-up up to 16 processors but plateaus thereafter.
  • Parallel ratcheting is efficient with up to 32 processors on large clusters.
  • Algorithm performance is largely independent of dataset size.

Conclusions:

  • Different parallel algorithms in POY exhibit distinct scaling properties.
  • Optimizing parallel strategies is crucial for efficient phylogenetic analyses on large computational clusters.