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Distributed machine learning: scaling up with coarse-grained parallelism

F J Provost1, D N Hennessy

  • 1Computer Science Department, University of Pittsburgh, PA 15260, USA.

Proceedings. International Conference on Intelligent Systems for Molecular Biology
|January 1, 1994
PubMed
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This study introduces a distributed learning system, CorPRL, to efficiently analyze large biological datasets. It leverages the invariant-partitioning property to overcome computational challenges in machine learning for big data.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Machine Learning

Background:

  • Machine learning (ML) is increasingly used in biological data analysis.
  • Scaling ML algorithms to large biological and medical datasets presents computational and memory challenges.
  • Existing methods struggle with the demands of big data in life sciences.

Purpose of the Study:

  • To address the scalability issues of machine learning in large biological datasets.
  • To develop a distributed learning system that can handle massive data volumes.
  • To introduce the invariant-partitioning property for efficient data subset analysis.

Main Methods:

  • Utilized ubiquitous workstation networks for distributed computing.
  • Introduced the invariant-partitioning property for data set partitioning.

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  • Developed a distributed learning system named CorPRL.
  • Employed interprocess communication for cooperative learning.

    • Main Results:

    • CorPRL effectively learns from very large datasets.
    • The invariant-partitioning property enables efficient data subset analysis.
    • Demonstrated effectiveness in analyzing data from two biological databases.
    • Overcame computational search effort and memory limitations.

    Conclusions:

    • Distributed learning systems can overcome scalability challenges in big biological data.
    • The invariant-partitioning property is a key enabler for efficient distributed ML.
    • CorPRL provides a viable solution for analyzing massive biological and medical datasets.