High-Performance Statistical Computing in the Computing Environments of the 2020s
AffiliationMel and Enid Zuckerman College of Public Health, University of Arizona
Graphics processing units (gpus)
High-performance statistical computing
MetadataShow full item record
PublisherInstitute of Mathematical Statistics
CitationKo, S., Zhou, H., Zhou, J. J., & Won, J.-H. (2022). High-Performance Statistical Computing in the Computing Environments of the 2020s. Statistical Science, 37(4), 494–518.
RightsCopyright © Institute of Mathematical Statistics, 2022.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractTechnological advances in the past decade, hardware and software alike, have made access to high-performance computing (HPC) easier than ever.We review these advances from a statistical computing perspective. Cloud computing makes access to supercomputers affordable. Deep learning software libraries make programming statistical algorithms easy and enable users to write code once and run it anywhere—from a laptop to a workstation with multiple graphics processing units (GPUs) or a supercomputer in a cloud. Highlighting how these developments benefit statisticians, we review recent optimization algorithms that are useful for high-dimensional models and can harness the power of HPC. Code snippets are provided to demonstrate the ease of programming. We also provide an easy-to-use distributed matrix data structure suitable for HPC. Employing this data structure, we illustrate various statistical applications including large-scale positron emission tomography and ℓ1-regularized Cox regression. Our examples easily scale up to an 8-GPU workstation and a 720-CPU-core cluster in a cloud. As a case in point, we analyze the onset of type-2 diabetes from the UK Biobank with 200,000 subjects and about 500,000 single nucleotide polymorphisms using the HPC ℓ1-regularized Cox regression. Fitting this half-million-variate model takes less than 45 minutes and reconfirms known associations. To our knowledge, this is the first demonstration of the feasibility of penalized regression of survival outcomes at this scale © Institute of Mathematical Statistics, 2022
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