A Tool for Creating and Parallelizing Bioinformatics Pipelines

Abstract

Bioinformatics pipelines enable hfr scientists to effectively analyze biological data through automated multi-step processes constructed by individual programs and databases. The huge amount of data and time consuming computations require effectively parallelized pipehnes to provide results within a reasonable time. To reduce researchers' programming burden for pipeline creation and parallelization, we developed the Bioinformatics Pipeline Generation and Parallelization Toolkit (B io Gent). A user needs only to create a pipehne definition file that describes the data processing sequence and input/output files. A program termed schedpipe in the BioGent toolkit takes the definition file and executes the designed procedure. Schedpipe automatically parallelizes the pipeline execution by performing independent data processing steps on muliple CPUs, and by decomposing big datasets into small chunks and processing them in parallel. Schedpipe controls program execution on multiple CPUs through a simple application programming interface (API) of the Parallel Job Manager (PJM) library. As a part of the BioGent toolkit, PJM was developed to effectively launch and monitor programs on multiple CPUs using a Message Passing Interface (MPI) protocol. The PJMAPI can also be used to parallelize other serial programs. A demonstration using PJM for parallelization shows 10% to 50% savings in time compared to an indigenous parallelization through a batch queuing system.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2007
Accession Number
ADP023782

Entities

People

  • Chenggang Yu
  • Paul A. Wilson

Organizations

  • United States Army Medical Research and Development Command

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Application Programming Interface
  • Application Software
  • Biomedical Information Systems
  • Computational Biology
  • Computer Programming
  • Computers
  • Data Processing
  • Databases
  • Department Of Defense
  • High Performance Computing
  • Neural Networks
  • Nucleic Acids
  • Parallel Computing
  • Pipelines
  • Proteins
  • Sequences
  • Technical Information Centers

Fields of Study

  • Computer science
  • Engineering

Readers

  • Distributed Systems and Data Platform Development
  • Parallel and Distributed Computing.