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Cluster Computing Applications Project Parallelizing BLAST. William Burke York College, City University of New York John Mugler and Stephen Scott Oak Ridge National Laboratory. Research Alliance of Minorities (RAM), Computer Science and Mathematics Division.

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Cluster Computing Applications Project Parallelizing BLAST

William Burke

York College, City University of New York

John Mugler and Stephen Scott

Oak Ridge National Laboratory

Research Alliance of Minorities (RAM), Computer Science and Mathematics Division


Parallelizing the blast algorithm feasible or not
Parallelizing the BLAST Algorithm: Feasible or Not?

Bioinformatics Research needs faster text string matching algorithms.

The purpose of this project is to analyze the BLAST algorithm:

Define the structure of BLAST.

State why it is a valuable Bioinformatics tool.

Explore parallelizations of BLAST.

BLAST matches query string fragments against a target database.

Eliminates need to run a full text string comparison.

Speeds up search database search time.

Several methods of parallelizing BLAST have been explored.


Introduction
Introduction

  • Cluster infrastructure

    • Open Source Cluster Application Resources (OSCAR)

    • Cluster, Command and Control (C3)

    • eXtreme TORC (XTORC)

  • Cluster applications

    • Bioinformatics Toolsets

    • Basic Local Alignment Sequence Tool (BLAST)


Infrastructure overview
Infrastructure Overview

Red Hat Linux 7.2

OSCAR 1.3

C3 - http://www.csm.ornl.gov/torc/C3/

LAM/MPI - http://www.lam-mpi.org/

Maui Scheduler - http://supercluster.org/maui/

MPICH - http://www-unix.mcs.anl.gov/mpi/mpich/

OpenSSH - http://www.openssh.com/

OpenSSL - http://www.openssl.org/

PBS - http://www.openpbs.org/

PVM - http://www.csm.ornl.gov/pvm/

SIS - http://www.sisuite.org/


Red hat linux 7 2
Red Hat Linux 7.2

Installation

Configuration

Administration

Network Configuration.

Performance Monitoring.

Creating Scripts.


Oscar 1 3 and c3 tools
OSCAR 1.3 and C3 Tools

OSCAR configures the head node.

OSCAR builds and configures compute nodes.

C3 reduces time and effort to operate and manage a cluster.


eXtreme TORC

eXtreme TORC powered by OSCAR

  • 65 Pentium IV Machines

  • Peak Performance: 129.7 GFLOPS

  • RAM memory: 50.152 GB

  • Disk Capacity: 2.68 TB

  • Dual interconnects

    • Gigabit & Fast Ethernet


The field of

Bioinformatics

needs faster string

matching algorithms


Applications overview
Applications Overview

  • BLAST a Bioinformatics tool.

    http://www.ncbi.nlm.nih.gov/BLAST/blast_overview.html

  • Parallelize BLAST’s algorithm.

BLAST


Blast a bioinformatics tool
BLAST a Bioinformatics Tool

What is BLAST?

A heuristic algorithm used for string matching query strings to a database.

How does BLAST algorithm work?

String fragmentation.

Statistical means for comparison.

How can you parallelize BLAST on a computational cluster?


The BLAST Search Algorithm

Query word (W = 3)

QUERY: GSVEDTTGSQSLAALLNKCKTPQGQRLVNQWIKWPLMDKNRIEERLNLVEAFVEDA

PQG 18

neighborhood PEG 15

words PRG 14

PKG 14

PMG 13 neighborhood

PSG 13 score threshold

PQN 12 ( T = 13 )

Etc...

QUERY STRING SLAALLNKCKTPQGQWLVNQWIKWPLMDKNRIEERLN 365

----L--++K-P-G--+-----+-------------N

n DATABASE STRING GSWNLAALDKDPMGDKNRIEERLNLVEAIKWPLMDJN 330


Parallelization of blast

PARALIGN™

Parallelization of BLAST

NBLAST SLRI Bioinformatics Toolkit

ParAlign

MOBLAST

www.usenix.org/publications/library/proceedings/ als2000/michalickova.html

DNA sequence matching processor


Conclusion

  • BLAST algorithm has a diverse family

  • of programs.

    • Several implementations exist for

  • parallelizing the BLAST algorithm.

    • Future work to include further

  • exploration of the various parallelized

  • BLAST algorithms on clusters.


  • Acknowledgements
    Acknowledgements

    I would like to extend my thanks to Stephen L. Scott, John Mugler, Thomas Naughton, and Brian Luethke for their invaluable mentoring, Michaelangelo Salcedo for his guidance, Debbie McCoy and Cheryl Hamby for their support in the RAM program.


    Disclaimer
    Disclaimer

    This research was performed under the Research Alliance for Minorities Program administered through the Computer Science and Mathematics Division, Oak Ridge National Laboratory. This Program is sponsored by the Mathematical, Information, and Computational Sciences Division; Office of Advanced Scientific Computing Research; U.S. Department of Energy. Oak Ridge National Laboratory is managed by UT-Battelle, LLC, for the U.S. Department of Energy under contract DE-AC05-00OR22725. This research used resources of the Center for Computational Sciences at Oak Ridge National Laboratory, which is supported by the Office of Science, U.S. Department of Energy. This work has been authored by a contractor of the U.S. Government under contract DE-AC05-00OR22725. Accordingly, the U.S. Government retains a nonexclusive, royalty-free license to publish or reproduce the published form of this contribution, or allow others to do so, for U.S. Government purposes.


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