CAREER: Efficient and Scalable Large Foundational Model Training on Supercomputers for Science

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Deep learning (DL) methods, especially the large foundational models, enable exciting new approaches to problems in many science and engineering disciplines, such as genomics, bioinformatics, meteorology, and natural language processing. Training foundational models at extreme scales is time-consuming, prone to low utilization with limited scalability, and human-effort demanding. This NSF CAREER project addresses the convergence, performance, and scalability gaps of large foundational model pre-training on supercomputers with innovative algorithms, systems, and interface design. In addition to the algorithm and computer system innovation, this project contributes to translational computer science by lowering the barrier of sizeable foundational model training and the time consumption of scientific deep learning, thus enabling significantly more scientific research to embrace large foundational models. The research results will be publicly available as open-source software to the broader community, with comprehensive documentation on the design and usage to help users from all domains.Technically, this NSF CAREER project has four research and educational thrusts: The first thrust focuses on new optimization techniques such as first-, second-, and mixed-order optimizers with potential approximation techniques to enhance time-to-convergence. The second thrust aims to enhance the scaling efficiency by designing novel sparsification algorithms that leverage the spatial and temporal patterns of gradients. The third thrust considers a new complex parallelism abstraction that transparently deploys large models across processors with near-optimal performance given the present capability of compute, interconnect, and I/O on a supercomputer. The fourth thrust designs educational activities, including a distributed DL system course, a DL tutorial, and a DL bootcamp targeting students and practitioners with different levels of expertise.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.