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Convergence of artificial intelligence and high performance computing on NSF-supported cyberinfrastructure
Journal of Big Data ( IF 8.1 ) Pub Date : 2020-10-16 , DOI: 10.1186/s40537-020-00361-2
E. A. Huerta , Asad Khan , Edward Davis , Colleen Bushell , William D. Gropp , Daniel S. Katz , Volodymyr Kindratenko , Seid Koric , William T. C. Kramer , Brendan McGinty , Kenton McHenry , Aaron Saxton

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.



中文翻译:

NSF支持的网络基础设施上的人工智能和高性能计算的融合

升级和建设大型科学设施的巨额投资需要对研发进行相应的投资,以设计算法和计算方法,以实现大数据时代的科学和工程突破。创新的人工智能(AI)应用程序为工业和技术领域的大数据挑战提供了转型解决方案,这些挑战现在推动着价值数十亿美元的产业,并且在塑造人类社会模式方面发挥着越来越重要的作用。随着AI不断发展成为具有统计和数学严格性的计算范例,显而易见的是,用于训练,验证,和测试已不再足以应对由科学机构带来的计算挑战,这些机构所产生的数据的速率和数量超过了可用的网络基础设施平台的计算能力。这种认识一直在推动AI和高性能计算(HPC)的融合,以减少见识时间,并能够对领域启发性AI架构和优化方案进行系统的研究,从而实现数据驱动的发现。在本文中,我们总结了该领域的最新进展,并描述了本文作者为加速和简化HPC平台在学术界和行业中设计和应用加速AI算法而领先的具体进展。这种认识一直在推动AI和高性能计算(HPC)的融合,以减少见识时间,并能够对领域启发性AI架构和优化方案进行系统的研究,从而实现数据驱动的发现。在本文中,我们总结了该领域的最新进展,并描述了本文作者为加速和简化HPC平台在学术界和行业中设计和应用加速AI算法而领先的具体进展。这种认识一直在推动AI和高性能计算(HPC)的融合,以减少见识时间,并能够对领域启发性AI架构和优化方案进行系统的研究,从而实现数据驱动的发现。在本文中,我们总结了该领域的最新进展,并描述了本文作者为加速和简化HPC平台在学术界和行业中设计和应用加速AI算法而领先的具体进展。

更新日期:2020-10-17
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