Scalable Parallelization of a Markov Coalescent Genealogy Sampler

@article{Davis2017ScalablePO,
  title={Scalable Parallelization of a Markov Coalescent Genealogy Sampler},
  author={Philip E. Davis and Adam M. Terwilliger and David Zeitler and Gregory Wolffe},
  journal={2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
  year={2017},
  pages={293-302}
}
Coalescent genealogy samplers are effective tools for the study of population genetics. They are used to estimate the historical parameters of a population based upon the sampling of present-day genetic information. A popular approach employs Markov chain Monte Carlo (MCMC) methods. While effective, these methods are very computationally intensive, often taking weeks to run. Although attempts have been made to leverage parallelism in an effort to reduce runtimes, they have not resulted in… CONTINUE READING

References

Publications referenced by this paper.
SHOWING 1-10 OF 34 REFERENCES

A general construction for parallelizing Metropolis-Hastings algorithms.

  • Proceedings of the National Academy of Sciences of the United States of America
  • 2014
VIEW 2 EXCERPTS

Efficient Computation of the Phylogenetic Likelihood Function  on the Intel MIC Architecture

  • 2014 IEEE International Parallel & Distributed Processing Symposium Workshops
  • 2014
VIEW 1 EXCERPT

A Generic Vectorization Scheme and a GPU Kernel for the Phylogenetic Likelihood Library

  • 2013 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum
  • 2013
VIEW 1 EXCERPT

Boosting the Performance of Bayesian Divergence Time Estimation with the Phylogenetic Likelihood Library

  • 2013 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum
  • 2013
VIEW 1 EXCERPT

Similar Papers

Loading similar papers…