Big Data Scalability of BayesPhylogenies on Harvard’s Ozone 12k Cores.

Manju Manjunathaiah, Andrew Meade, R Thavarajan, P Protopapas, R Dave

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Computational Phylogenetics is classed as a grand challenge data driven problem in the fourth paradigm of scientific discovery due to the exponential growth in genomic data, the computational challenge and the potential for vast impact on data driven biosciences. Petascale and Exascale computing offer the prospect of scaling Phylogenetics to big data levels. However the computational complexity of even approximate Bayesian methods for phylogenetic inference requires scalable analysis for big data applications. There is limited study on the scalability characteristics of existing computational models for petascale class massively parallel computers. In this paper we present strong and weak scaling performance analysis of BayesPhylogenies on Harvard's Ozone 12k cores. We perform evaluations on multiple data sizes to infer the scaling complexity and find that strong scaling techniques along with novel methods for communication reduction are necessary if computational models are to overcome limitations on emerging complex parallel architectures with multiple levels of concurrency. The results of this study can guide the design and implementation of scalable MCMC based computational models for Bayesian inference on emerging petascale and exascale systems.

Original languageEnglish
Title of host publicationBIOINFORMATICS 2019 - 10th International Conference on Bioinformatics Models, Methods and Algorithms, Proceedings; Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019
EditorsElisabetta De Maria, Hugo Gamboa, Ana Fred
Place of PublicationPrague, Czech Republic
Number of pages6
ISBN (Electronic)9789897583537
Publication statusPublished - 23 Feb 2019


  • Big Data
  • Exascale
  • Phylogenetics


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