Technological 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 -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 -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.
This research was partially funded by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2019R1A2C1007126, JHW; 2020R1A6A3A03037675, SK), the Collaboratory Fellowship program of the UCLA Institute for Quantitative & Computational Bioscience (SK), AWS Cloud Credit for Research (SK and JHW), and grants from National Institutes of Health (R35GM141798, HZ; R01HG006139, HZ and JJZ; K01DK106116, JJZ; R21HL150374, JJZ) and National Science Foundation (DMS-2054253, HZ and JJZ).
This article is partly based on the first author’s doctoral dissertation (Ko, 2020).
Seyoon Ko. Hua Zhou. Jin J. Zhou. Joong-Ho Won. "High-Performance Statistical Computing in the Computing Environments of the 2020s." Statist. Sci. 37 (4) 494 - 518, November 2022. https://doi.org/10.1214/21-STS835