人工智能和高性能计算在NSF支持的网络基础设施上的融合
Convergence of Artificial Intelligence and High Performance Computing on NSF-supported Cyberinfrastructure
摘要：
为升级和建设大规模科学设施而进行的重大投资，要求在研发方面进行相应的投资，以设计算法和计算方法，从而在大数据时代实现科学和工程突破。创新的人工智能（AI）应用为工业和技术中的大数据挑战提供了转型解决方案，这些解决方案现在推动了数十亿美元的产业，并在塑造人类社会模式方面发挥着越来越大的作用。随着人工智能不断发展成为一种具有统计和数学严谨性的计算范式，很明显，用于训练、验证和测试的单GPU解决方案不再足以应对科学设施带来的巨大计算挑战，这些科学设施产生的数据速度和数量超过了可用网络基础设施平台的计算能力。这一认识推动了人工智能和高性能计算（HPC）的融合，以减少洞察时间，并对领域启发的人工智能架构和优化方案进行系统研究，以实现数据驱动的发现。在本文中，我们总结了这一领域的最新发展，并描述了本文作者在加速和简化HPC平台的使用以在学术界和工业界设计和应用加速AI算法方面所取得的具体进展。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
