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Parallel Mapping Approaches for GNUMAP.

Nathan L Clement1, Mark J Clement, Quinn Snell

  • 1Department of Computer Science, University of Texas, Austin, TX 78712.

Proceedings. IPDPS (Conference)
|February 12, 2013
PubMed
Summary
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Large-scale genome mapping is memory-intensive. Parallel computing using Message Passing Interface (MPI) with multi-threading significantly reduces run-time and memory usage, achieving linear speedup for genome analysis.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Accurate genome mapping is crucial for SNP calling and gene expression studies.
  • Current whole-genome mapping methods face limitations due to high memory requirements and long processing times.
  • Parallel computing offers a potential solution to overcome these computational bottlenecks.

Purpose of the Study:

  • To evaluate and compare different parallel implementations for whole-genome read mapping.
  • To assess the memory footprint, load balancing, and accuracy of various parallel approaches.
  • To identify the most efficient parallel strategy for large-scale genomic analyses.

Main Methods:

  • Implementation of parallel algorithms using Message Passing Interface (MPI) and multi-threading.

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  • Systematic comparison of different parallelization strategies.
  • Performance evaluation based on memory usage, processing speed, and mapping accuracy.

    • Main Results:

    • Parallel implementations effectively distribute memory and processing loads across multiple processors.
    • The combined use of MPI and multi-threading demonstrated significant improvements in performance.
    • Linear speedup was observed with up to 256 processors when using MPI with multi-threading.

    Conclusions:

    • Parallel computing, particularly MPI with multi-threading, is a viable solution for memory and time constraints in large-scale genome mapping.
    • Optimized parallel strategies enhance the efficiency of SNP calling and expression studies.
    • This approach facilitates more accurate and scalable genomic analyses.