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Exploiting Thread-Level and Instruction-Level Parallelism to Cluster Mass Spectrometry Data using Multicore

Fahad Saeed1, Jason D Hoffert2, Trairak Pisitkun3

  • 1Department of Computer Science and, Department of Electrical & Computer Engineering, Western Michigan University, Kalamazoo, MI USA ; Epithelial Systems Biology Laboratory, National Heart Lung and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, MD USA.

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|July 22, 2014
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This summary is machine-generated.

A new parallel algorithm, P-CAMS, significantly speeds up mass spectrometry data clustering by using multicore processors and intelligent comparisons. This method enhances the efficiency of analyzing complex biological samples, improving peptide identification.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Analytical Chemistry

Background:

  • Mass spectrometry generates vast amounts of peptide spectral data.
  • Redundancy and low signal-to-noise ratios complicate data analysis and peptide mapping.
  • Clustering algorithms are essential for managing complex mass spectrometry datasets.

Purpose of the Study:

  • To address the computational inefficiency of existing clustering methods for large-scale mass spectrometry data.
  • To present a parallel algorithm, P-CAMS, for faster and more efficient data clustering.
  • To improve the analysis of complex biological samples in mass spectrometry.

Main Methods:

  • Developed a parallel algorithm (P-CAMS) utilizing thread-level and instruction-level parallelism on multicore architectures.
  • Employed intelligent matrix completion to reduce the number of spectral comparisons.
  • Implemented a load-balanced scheme leveraging spatial peak locations for cache and core mapping.
  • Utilized Single Instruction Multiple Data (SIMD) for massive parallelism within threads.

Main Results:

  • P-CAMS achieved substantial reductions in running times for clustering mass spectrometry data.
  • Demonstrated super-linear speedups through a carefully crafted load-balanced scheme.
  • Showcased the effectiveness of combining SIMD and thread-level parallelism for multicore architectures.
  • Validated P-CAMS's quality and consistency with real-world datasets compared to serial methods.

Conclusions:

  • P-CAMS offers a powerful and efficient solution for clustering large-scale mass spectrometry data.
  • The parallel approach significantly accelerates the analysis of complex biological samples.
  • This method enhances the accuracy and speed of peptide identification from mass spectrometry experiments.