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.
Highlights
The big data revolution disrupted the digital and computing landscape in the early 2010s [1]
The quest for novel pattern recognition algorithms [5,6,7] that sift through large, Huerta et al J Big Data (2020) 7:88 high-quality data sets eventually led to a disruptive combination of deep learning and graphics processing units (GPUs) that enabled a rapid succession of advances in computer vision, speech recognition, natural language processing, and robotics, to mention a few [3, 8]
We have explored whether the methods we have used in the context of HardwareAccelerated Learning (HAL) and Bridges-Artificial Intelligence (AI) may work in other high performance computing (HPC) platforms optimized for AI research
Summary
The big data revolution disrupted the digital and computing landscape in the early 2010s [1]. Within just a few years, the curation of high-quality data sets, e.g., ImageNet [9]; GPU-accelerated computing [10]; open source software platforms—TensorFlow [11], PyTorch [12] among others—to design, train, validate and test AI models; improved AI architectures and novel techniques [13, 14] to enhance the performance of deep neural networks, such as robust optimizers [15] and regularization techniques [16], led to the rapid development of AI tools that significantly outperform other signal processing tools on many tasks [17, 18].
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