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 ℓ 1-regularized Cox regression. Our examples easily scale up to an 8-GPU workstation and a 720-CPU-core cluster in a cloud. Employing this data structure, we illustrate various statistical applications including large-scale positron emission tomography and ℓ 1-regularized Cox regression.
We also provide an easy-to-use distributed matrix data structure suitable for HPC. Code snippets are provided to demonstrate the ease of programming. 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.
#MPI VS GRPC VS REST SOFTWARE#
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. Cloud computing makes access to supercomputers affordable. We review these advances from a statistical computing perspective. Technological advances in the past decade, hardware and software alike, have made access to high-performance computing (HPC) easier than ever.
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