Scalable Gaussian Processes, GPyTorch Application Benchmarking, and Targeted Adaptive Design (TAD) on ThetaGPU

Technical Report · Wed Nov 30 04:00:00 EST 2022

DOI:https://doi.org/10.2172/1901824· OSTI ID:1901824

Tian, Yuyang ^[1]; Graziani, Carlo ^[1]; Ngom, Marième ^[1]

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1. Argonne National Laboratory (ANL), Argonne, IL (United States)

We aim at showcasing the scalability of Gaussian Process (GP). The naive GP implementation scales cubically with data size, which can be prohibitive, so GP has not heretofore been considered suitable for very large-scale problem settings. We take advantage of GPyTorch, a library for scalable GPs built on top of PyTorch that incorporates GPU acceleration. With GPyTorch, one can achieve nearly linear scaling with structured kernel interpolation (SKI) and constant-time predictive covariances computation with LanczOs Variance Estimates (LOVE) while preserving accuracy. We also take advantage of the computational power of ThetaGPU, a supercomputer of Argonne Leadership Computing Facility (ALCF). In addition, we implement a scalable, GPU-ready version of Targeted Adaptive Design (TAD), a GP-based data-driven algorithm that efficiently searches the control space of an advanced manufacturing experiment for settings capable of producing a required design within a specified tolerance, despite the poorly known mapping from control settings to design. We finally show our benchmarking for GPyTorch and TAD performance on CPU vs. ThetaGPU and discuss the results and implications.

• Research Organization: Argonne National Laboratory (ANL), Argonne, IL (United States). Argonne Leadership Computer Facility (ALCF)
• Sponsoring Organization: USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 1901824
• Report Number(s): ANL-22/89; 180080
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING