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CCC-GPU: A graphics processing unit (GPU)-accelerated nonlinear correlation coefficient for large-scale

Haoyu Zhang1, Kevin Fotso2, Milton Pividori1

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Summary
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This study introduces CCC-GPU, a GPU-accelerated tool for calculating correlation coefficients in biological data. It efficiently detects complex, non-linear relationships in large datasets.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Analyzing complex biological data requires correlation coefficients that capture non-linear relationships.
  • Efficient computational tools are essential for managing large biological datasets.

Purpose of the Study:

  • To introduce CCC-GPU, a GPU-accelerated implementation of the Clustermatch Correlation Coefficient (CCC).
  • To provide a high-performance tool for computing correlation coefficients in biological data.

Main Methods:

  • GPU-accelerated computation of the Clustermatch Correlation Coefficient (CCC).
  • Implementation designed to handle mixed data types and detect non-linear relationships.

Main Results:

  • CCC-GPU offers significant speed improvements compared to previous implementations.
  • The tool effectively computes correlation coefficients for mixed data types.
  • Demonstrates capability in detecting non-linear relationships within biological data.

Conclusions:

  • CCC-GPU provides an efficient and effective solution for correlation analysis in large-scale biological data.
  • The GPU acceleration significantly enhances computational performance for correlation coefficient calculations.