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Big data in multi-block data analysis: An approach to parallelizing Partial Least Squares Mode B algorithm.

Alba Martinez-Ruiz1, Cristina Montañola-Sales2,3

  • 1Universidad Católica de la Santísima Concepción, Alonso de Ribera 2850, Concepción, Chile.

Heliyon
|June 12, 2019
PubMed
Summary
This summary is machine-generated.

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Parallelizing Partial Least Squares (PLS) Mode B enables efficient structural equation modeling (SEM) for big data. This approach significantly reduces computation time on distributed systems, achieving speedups up to 121x.

Area of Science:

  • Computational statistics
  • Data science
  • High-performance computing

Background:

  • Partial Least Squares (PLS) Mode B is a robust algorithm for estimating structural equation models (SEMs).
  • Analyzing large, distributed datasets with traditional methods presents significant computational challenges.
  • Efficient parallelization strategies are crucial for handling big data in statistical modeling.

Purpose of the Study:

  • To parallelize the PLS Mode B algorithm for large-scale, distributed data.
  • To evaluate the scalability and performance of the parallelized algorithm in a supercomputing environment.
  • To demonstrate the feasibility of estimating SEMs with big data using advanced multi-block analysis techniques.

Main Methods:

  • Implementation of the PLS Mode B algorithm using the pbdR library for parallel computing.
Keywords:
Computational mathematicsComputer science

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  • Testing the algorithm across various data distribution schemes and processor grid configurations.
  • Performance evaluation based on elapsed computation times and speedup compared to CPU implementations.

    • Main Results:

    • The parallelized PLS Mode B algorithm demonstrates significant scalability on distributed data.
    • Optimal performance was observed with square-blocking factors on square processor grids and non-square factors on column-based grids.
    • Speedups of up to 121x were achieved compared to the sequential CPU implementation, depending on the configuration.

    Conclusions:

    • Parallel computing significantly enhances the efficiency of PLS Mode B for big data analysis.
    • The pbdR library provides a versatile platform for fine-grained performance tuning.
    • State-of-the-art multi-block data analysis algorithms can effectively estimate SEMs for large datasets.