University of Hertfordshire

By the same authors

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

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

Standard

Big Data Scalability of BayesPhylogenies on Harvard’s Ozone 12k Cores. / Manjunathaiah, Manju; Meade, Andrew; Thavarajan, R; Protopapas, P; Dave, R.

BIOINFORMATICS 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. ed. / Elisabetta De Maria; Hugo Gamboa; Ana Fred. Prague, Czech Republic , 2019. p. 143-148.

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

Harvard

Manjunathaiah, M, Meade, A, Thavarajan, R, Protopapas, P & Dave, R 2019, Big Data Scalability of BayesPhylogenies on Harvard’s Ozone 12k Cores. in E De Maria, H Gamboa & A Fred (eds), BIOINFORMATICS 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. Prague, Czech Republic , pp. 143-148. https://doi.org/10.5220/0007249601430148

APA

Manjunathaiah, M., Meade, A., Thavarajan, R., Protopapas, P., & Dave, R. (2019). Big Data Scalability of BayesPhylogenies on Harvard’s Ozone 12k Cores. In E. De Maria, H. Gamboa, & A. Fred (Eds.), BIOINFORMATICS 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 (pp. 143-148). https://doi.org/10.5220/0007249601430148

Vancouver

Manjunathaiah M, Meade A, Thavarajan R, Protopapas P, Dave R. Big Data Scalability of BayesPhylogenies on Harvard’s Ozone 12k Cores. In De Maria E, Gamboa H, Fred A, editors, BIOINFORMATICS 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. Prague, Czech Republic . 2019. p. 143-148 https://doi.org/10.5220/0007249601430148

Author

Manjunathaiah, Manju ; Meade, Andrew ; Thavarajan, R ; Protopapas, P ; Dave, R. / Big Data Scalability of BayesPhylogenies on Harvard’s Ozone 12k Cores. BIOINFORMATICS 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. editor / Elisabetta De Maria ; Hugo Gamboa ; Ana Fred. Prague, Czech Republic , 2019. pp. 143-148

Bibtex

@inproceedings{1f8ef1fd09c9451b9f4216f228690bfc,
title = "Big Data Scalability of BayesPhylogenies on Harvard{\textquoteright}s Ozone 12k Cores.",
abstract = "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.",
keywords = "Big Data, Exascale, Phylogenetics",
author = "Manju Manjunathaiah and Andrew Meade and R Thavarajan and P Protopapas and R Dave",
year = "2019",
month = feb,
day = "23",
doi = "10.5220/0007249601430148",
language = "English",
pages = "143--148",
editor = "{De Maria}, Elisabetta and Hugo Gamboa and Ana Fred",
booktitle = "BIOINFORMATICS 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",

}

RIS

TY - GEN

T1 - Big Data Scalability of BayesPhylogenies on Harvard’s Ozone 12k Cores.

AU - Manjunathaiah, Manju

AU - Meade, Andrew

AU - Thavarajan, R

AU - Protopapas, P

AU - Dave, R

PY - 2019/2/23

Y1 - 2019/2/23

N2 - 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.

AB - 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.

KW - Big Data

KW - Exascale

KW - Phylogenetics

UR - http://www.scopus.com/inward/record.url?scp=85064596541&partnerID=8YFLogxK

U2 - 10.5220/0007249601430148

DO - 10.5220/0007249601430148

M3 - Conference contribution

SP - 143

EP - 148

BT - BIOINFORMATICS 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

A2 - De Maria, Elisabetta

A2 - Gamboa, Hugo

A2 - Fred, Ana

CY - Prague, Czech Republic

ER -