tutorial

High performance phylogenetic analysis on CUDA-compatible GPUs

Authors:

Cheng Ling,

Khaled Benkrid,

Tsuyoshi HamadaAuthors Info & Claims

ACM SIGARCH Computer Architecture News, Volume 40, Issue 5

Pages 52 - 57

https://doi.org/10.1145/2460216.2460226

Published: 25 March 2012 Publication History

Get Access

Abstract

The operation of phylogenetic analysis aims to investigate the evolution and relationships among species. It is widely used in the fields of system biology and comparative genomics. However, phylogenetic analysis is also a computationally intensive operation as the number of tree topology grows in a factorial way with the number of species involved. Therefore, due to the large number of species in the real world, the computational burden has largely thwarted phylogenetic reconstruction. In this paper, we describe the detailed GPU-based multi-threaded design and implementation of a Markov Chain Monte Carlo (MCMC) maximum likelihood algorithm for phylogenetic analysis on a set of aligned nucleotide sequences. The implementation is based on the framework of the most widely used phylogenetic analysis tool, namely MrBayes. The proposed approach resulted in 6x-8x speed-up on an NVidia Geforce 460 GTX GPU compared to an optimized GPP-based software implementation running on a desktop computer with a single Intel Xeon 2.53 GHz CPU and 6.0 GB RAM.

References

[1]

Saitou M. and Nei N.: The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol, 1987, 4:406--425.

Google Scholar

[2]

Farris J.S.: Estimating phylogenetic trees from distance matrices. American Nature, 1967, 155:279--284.

Google Scholar

[3]

Felsentein J.: Evolutionary trees from DNA sequnces: a maximum likelihood approach, J.Mol.Evol, 1981, 17:368--376.

Crossref

Google Scholar

[4]

Fitch W.M.: Toward defining the course of evolution: Minumum changer for a specfic tree topology. Systematic Zoology, 1971, 20:406--416.

Crossref

Google Scholar

[5]

Download website for MrBayes, http://mrbayes.sourceforge.net/download.php.

Google Scholar

[6]

Download website for PAUP, http://paup.csit.fsu.edu/down.html.

Google Scholar

[7]

Thompson J.D., Higgins D.G. and Gibson T.J.: CLUSTALW: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 1994. 22:4673--4680.

Crossref

Google Scholar

[8]

Box G.E.P. and Tiao G.C.: Bayesian Inference in Statistical Analysis. Wiley, 1973, ISBN 0-471-57428-7.

Google Scholar

[9]

Hastings W.K.: Monte Carlo Sampling Methods Using Markov Chains and Their Applications". Biometrika, 1970, 57(1):97--109.

Crossref

Google Scholar

[10]

Altekar G., Dwarkadas S., Huelsenbeck J.P. and Ronquist F.:Parallel Metropolis coupled Markov chain Monte Carlo for Bayesian phylogenetic inference. Bioinformatics 2004, 20(3):407--415.

Digital Library

Google Scholar

[11]

Zhou J.F., Liu X.G., Stones D.S., Xie Q. and Wang G.: MrBayes on a Graphics Processing Unit. Bioinformatics 2011, 27(9): 1255--1261.

Digital Library

Google Scholar

[12]

Felsenstein J. and Churchill G.A.: A Hidden Markov Model approach to variation among sites in rate of evolution, and the branching order in hominoidea. Molecular Biology and Evolution, 1996, 13(1):93--104.

Crossref

Google Scholar

[13]

Suchard M.A. and Rambaut A.: Many-core algorithms for statistical phylogenetics. Bioinformatics, 2009, 25(11): 1370--1376.

Digital Library

Google Scholar

Cited By

View all

Santander-Jiménez SVega-Rodríguez MZahinos-Márquez ASousa L(2020)GPU acceleration of Fitch’s parsimony on protein data: from Kepler to TuringThe Journal of Supercomputing10.1007/s11227-020-03225-x76:12(9827-9853)Online publication date: 1-Dec-2020
https://dl.acm.org/doi/10.1007/s11227-020-03225-x

Index Terms

High performance phylogenetic analysis on CUDA-compatible GPUs
1. Computing methodologies
  1. Computer graphics
    1. Graphics systems and interfaces
      1. Graphics processors
2. Hardware
  1. Hardware validation

Recommendations

High-performance cone beam reconstruction using CUDA compatible GPUs

Compute unified device architecture (CUDA) is a software development platform that allows us to run C-like programs on the nVIDIA graphics processing unit (GPU). This paper presents an acceleration method for cone beam reconstruction using CUDA ...
Performance study on CUDA GPUs for parallelizing the local ensemble transformed Kalman filter algorithm

Modern graphics cards provide computational capabilities that exceed current CPUs. As one of the computational intensive problems, numerical weather prediction has the opportunity to benefit from the massive number of threads and large memory throughput ...
Nekbone performance on GPUs with OpenACC and CUDA Fortran implementations

We present a hybrid GPU implementation and performance analysis of Nekbone, which represents one of the core kernels of the incompressible Navier---Stokes solver Nek5000. The implementation is based on OpenACC and CUDA Fortran for local parallelization ...

Comments

Information & Contributors

Information

Published In

cover image ACM SIGARCH Computer Architecture News

ACM SIGARCH Computer Architecture News Volume 40, Issue 5

ACM SIGARCH Computer Architecture News/HEART '12

December 2012

110 pages

ISSN:0163-5964

DOI:10.1145/2460216

Issue’s Table of Contents

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 March 2012

Published in SIGARCH Volume 40, Issue 5

Check for updates

Qualifiers

Tutorial

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
146
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)2

Reflects downloads up to 30 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Santander-Jiménez SVega-Rodríguez MZahinos-Márquez ASousa L(2020)GPU acceleration of Fitch’s parsimony on protein data: from Kepler to TuringThe Journal of Supercomputing10.1007/s11227-020-03225-x76:12(9827-9853)Online publication date: 1-Dec-2020
https://dl.acm.org/doi/10.1007/s11227-020-03225-x

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

High-performance cone beam reconstruction using CUDA compatible GPUs

Performance study on CUDA GPUs for parallelizing the local ensemble transformed Kalman filter algorithm

Nekbone performance on GPUs with OpenACC and CUDA Fortran implementations

Comments

Published In

Publisher

Publication History

Check for updates

Qualifiers

Other Metrics

Article Metrics

Other Metrics

Cited By

Login options

Full Access

PDF

eReader

Abstract

References

Cited By

Index Terms

Recommendations

High-performance cone beam reconstruction using CUDA compatible GPUs

Performance study on CUDA GPUs for parallelizing the local ensemble transformed Kalman filter algorithm

Nekbone performance on GPUs with OpenACC and CUDA Fortran implementations

Comments

Information

Published In

Publisher

Publication History

Check for updates

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Get Access

Login options

Full Access

View options

PDF

eReader

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations