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Parallelized calculation of permutation tests.

Markus Ekvall1, Michael Höhle2, Lukas Käll1

  • 1Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, 171 21 Solna, Sweden.

Bioinformatics (Oxford, England)
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Summary
This summary is machine-generated.

Parallelizing the Green algorithm significantly speeds up permutation tests, making them a viable and attractive alternative for analyzing medium-sized datasets. This computational enhancement addresses previous speed limitations, improving statistical analysis efficiency.

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Area of Science:

  • Statistical computing
  • Computational statistics
  • Algorithm development

Background:

  • Permutation tests are valuable for assessing statistical significance with minimal distributional assumptions.
  • Traditional permutation tests are computationally intensive, limiting their application with larger sample sizes.
  • Dynamic programming algorithms reduced computation time but haven't led to widespread adoption for medium sample sizes.

Purpose of the Study:

  • To enhance the computational efficiency of exact permutation tests.
  • To make permutation tests a more practical and attractive statistical tool for medium sample sizes.
  • To address the performance limitations of existing permutation test algorithms.

Main Methods:

  • Developed a computational parallelization of the Green algorithm, a dynamic programming-based permutation test.
  • Restructured the Green algorithm to enable parallel execution.
  • Implemented the parallelized algorithm for execution on Graphics Processing Units (GPUs).

Main Results:

  • Achieved orders-of-magnitude speed-up in permutation test execution time.
  • Demonstrated that the parallelized Green algorithm is efficient for sample sizes up to hundreds.
  • The computational time is no longer a limiting factor for these sample sizes.

Conclusions:

  • The parallelized Green algorithm offers a significant computational improvement for permutation tests.
  • This enhancement makes permutation tests a competitive and attractive alternative to methods like the Mann-Whitney U-test.
  • The method broadens the applicability of exact permutation tests in statistical analysis.