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High-Performance Statistical Computing in the Computing Environments of the 2020s.

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Summary
This summary is machine-generated.

Recent advances in high-performance computing (HPC) and deep learning software make complex statistical modeling accessible. These tools enable efficient analysis of large datasets, like the UK Biobank diabetes study, using penalized regression.

Keywords:
ADMMCox regressionHigh-performance statistical computingMM algorithmsPDHGcloud computingdeep learninggraphics processing units (GPUs)

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Area of Science:

  • Statistical computing
  • High-performance computing (HPC)
  • Machine learning

Background:

  • Technological advancements have increased accessibility to high-performance computing (HPC).
  • Cloud computing and deep learning libraries simplify programming for statistical algorithms.
  • These developments offer significant benefits for statisticians working with large datasets.

Purpose of the Study:

  • To review recent advances in HPC and statistical computing.
  • To highlight how these developments benefit statisticians.
  • To demonstrate the application of HPC in analyzing high-dimensional statistical models.

Main Methods:

  • Review of hardware and software advances in HPC.
  • Development of a distributed matrix data structure for HPC.
  • Application of optimization algorithms for high-dimensional models on HPC resources.
  • Implementation of penalized regression for survival outcomes using HPC.

Main Results:

  • Statistical algorithms can be programmed easily and run on diverse hardware, from laptops to supercomputers.
  • Provided code snippets and a distributed matrix data structure facilitate HPC programming.
  • Demonstrated scalability of statistical applications, including positron emission tomography and ℓ1-regularized Cox regression, on multi-GPU and multi-core cluster systems.
  • Successfully fitted a half-million-variate ℓ1-regularized Cox regression model to UK Biobank data in under 45 minutes, analyzing type-2 diabetes onset.

Conclusions:

  • Recent technological advances have democratized access to HPC for statistical analysis.
  • The developed methods and tools enable efficient analysis of large-scale, high-dimensional statistical models.
  • This work demonstrates the feasibility of penalized regression for survival outcomes at unprecedented scales.