Convergence of artificial intelligence and high performance computing on NSF-supported cyberinfrastructure

Abstract

Significant investments to upgrade and construct large-scale scientific facilities demand commensurate investments in R&D to design algorithms and computing approaches to enable scientific and engineering breakthroughs in the big data era. Innovative Artificial Intelligence (AI) applications have powered transformational solutions for big data challenges in industry and technology that now drive a multi-billion dollar industry, and which play an ever increasing role shaping human social patterns. As AI continues to evolve into a computing paradigm endowed with statistical and mathematical rigor, it has become apparent that single-GPU solutions for training, validation, and testing are no longer sufficient for computational grand challenges brought about by scientific facilities that produce data at a rate and volume that outstrip the computing capabilities of available cyberinfrastructure platforms. This realization has been driving the confluence of AI and high performance computing (HPC) to reduce time-to-insight, and to enable a systematic study of domain-inspired AI architectures and optimization schemes to enable data-driven discovery. In this article we present a summary of recent developments in this field, and describe specific advances that authors in this article are spearheading to accelerate and streamline the use of HPC platforms to design and apply accelerated AI algorithms in academia and industry.

A New Generation of ResNet Model Based on Artificial Intelligence and Few Data Driven and Its Construction in Image Recognition Model

“…Therefore, the development of open, standardized, and intelligent [ 3 ] green tea classifications and identification methods is an inevitable trend. New classification and assessment methods for green tea have been emerging, such as physicochemical review methods [ 4 , 5 ], fingerprinting assessment methods [ 6 , 7 ], intelligent sensory review methods [ 8 , 9 ], and infrared spectral imaging technology detection methods [ 10 , 11 ], but these methods have their limitations to a certain extent, such as relevant instruments and cumbersome and complicated operations, and most of them are based on the overall tea leaves. It is necessary to propose an objective, simple, fast, and low-cost method for green tea classification, since most of them are based on the whole tea leaves for review, which requires specific and time-consuming requirements.…”

National Power After AI

“…Consequently, commercial cloud compute resources may better serve the U.S. Government in deploying certain kinds of AI technologies, although potentially promising efforts to improve the use of U.S. HPC assets for AI are also underway. 27 Effective use will depend, too, on accessible software tools for using cloud compute systemswhich may prove to be comparable to process and tooling approaches developed to make factories effective during industrialization in the United States. 28 Compute resources can flow more easily than many traded goods.…”

Inference-Optimized AI and High Performance Computing for Gravitational Wave Detection at Scale

“…Over the last few years, it has become apparent that the convergence of artificial intelligence (AI) and innovative computing provides the means to tackle computational grand challenges that have been exacerbated with the advent of large scale scientific facilities, and which will not be met by the ongoing deployment of exascale HPC systems alone (Asch et al, 2018 ; Huerta et al, 2020 ). As described in recent reviews (Huerta and Zhao, 2020 ; Cuoco et al, 2021 ), AI and high performance computing (HPC) as well as edge computing have been showcased to enable gravitational wave detection with the same sensitivity than template-matching algorithms, but orders of magnitude faster and at a fraction of the computational cost.…”