1 / 15

Cluster Computing Applications Project Parallelizing BLAST

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.

gerald
Télécharger la présentation

Cluster Computing Applications Project Parallelizing BLAST

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. 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.

  3. 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)

  4. 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/

  5. Red Hat Linux 7.2 Installation Configuration Administration Network Configuration. Performance Monitoring. Creating Scripts.

  6. 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.

  7. 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

  8. The field of Bioinformatics needs faster string matching algorithms

  9. Applications Overview • BLAST a Bioinformatics tool. http://www.ncbi.nlm.nih.gov/BLAST/blast_overview.html • Parallelize BLAST’s algorithm. BLAST

  10. 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?

  11. 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

  12. PARALIGN™ Parallelization of BLAST NBLAST SLRI Bioinformatics Toolkit ParAlign MOBLAST www.usenix.org/publications/library/proceedings/ als2000/michalickova.html DNA sequence matching processor

  13. 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.

  14. 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.

  15. 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.

More Related